Articles | Volume 23, issue 24
https://doi.org/10.5194/acp-23-15413-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-23-15413-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Opinion
| Highlight paper
| 
18 Dec 2023
Opinion | Highlight paper |
| 18 Dec 2023
| 18 Dec 2023
Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts
Blaž Gasparini
CORRESPONDING AUTHOR
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
Sylvia C. Sullivan
Department of Chemical and Environmental Engineering, University of Arizona, Tucson, Arizona, USA
Adam B. Sokol
Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA
Bernd Kärcher
Institut für Physik der Atmosphäre, DLR Oberpfaffenhofen, Wessling, Germany
Eric Jensen
NOAA Chemical Sciences Laboratory, Boulder, CO, USA
Dennis L. Hartmann
Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA
Related authors
Aiko Voigt, Stefanie North, Blaz Gasparini, and Seung-Hee Ham
EGUsphere, https://doi.org/10.5194/egusphere-2023-2612, https://doi.org/10.5194/egusphere-2023-2612, 2023
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Clouds shape weather and climate by interacting with photons, which changes temperatures within the atmosphere. We assess how well CMIP6 climate models capture this radiative heating by clouds within the atmosphere. While we find large differences among models, especially in cold regions of the atmosphere with abundant ice clouds, we also demonstrate that physical understanding allows us to predict the response of clouds and their radiative heating near the tropopause to climate change.
Suvarna Fadnavis, Rolf Müller, Gayatry Kalita, Matthew Rowlinson, Alexandru Rap, Jui-Lin Frank Li, Blaž Gasparini, and Anton Laakso
Atmos. Chem. Phys., 19, 9989–10008, https://doi.org/10.5194/acp-19-9989-2019, https://doi.org/10.5194/acp-19-9989-2019, 2019
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This paper highlights the impact of Asian anthropogenic emission changes in SO2 on sulfate loading in the Asian upper troposphere–lower stratosphere from a global chemistry–climate model and satellite remote sensing. Estimated seasonal mean direct radiative forcing at the top of the atmosphere induced by the increase in Indian SO2 is −0.2–−1.5 W m2 over India. Chinese SO2 emission reduction leads to a positive radiative forcing of ~0.6–6 W m2 over China. It will likely decrease Indian rainfall.
Suvarna Fadnavis, Gayatry Kalita, K. Ravi Kumar, Blaž Gasparini, and Jui-Lin Frank Li
Atmos. Chem. Phys., 17, 11637–11654, https://doi.org/10.5194/acp-17-11637-2017, https://doi.org/10.5194/acp-17-11637-2017, 2017
Short summary
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In this study, the model simulations show that monsoon convection over the Bay of Bengal, the South China Sea and southern flanks of the Himalayas transports Asian carbonaceous aerosol into the UTLS. Carbonaceous aerosol induces enhancement in heating rate, vertical velocity and water vapor transport in the UTLS. Doubling of carbonaceous aerosols creates an anomalous warming over the TP. It generates monsoon Hadley circulation and thus increases precipitation over India and northeast China.
Blaž Gasparini, Steffen Münch, Laure Poncet, Monika Feldmann, and Ulrike Lohmann
Atmos. Chem. Phys., 17, 4871–4885, https://doi.org/10.5194/acp-17-4871-2017, https://doi.org/10.5194/acp-17-4871-2017, 2017
Short summary
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Cirrus clouds have, unlike other cloud types, a warming impact on climate. Decreasing their frequency therefore leads to a cooling effect. Cirrus ice crystals grow larger when formed on solid aerosols, inducing a shorter cloud lifetime.
We compare simplified simulations of stripping cirrus out of the sky with simulations of seeding by aerosol injections. While we find the surface climate responses to be similar, the changes in clouds and cloud properties differ significantly.
Aiko Voigt, Stefanie North, Blaz Gasparini, and Seung-Hee Ham
EGUsphere, https://doi.org/10.5194/egusphere-2023-2612, https://doi.org/10.5194/egusphere-2023-2612, 2023
Short summary
Short summary
Clouds shape weather and climate by interacting with photons, which changes temperatures within the atmosphere. We assess how well CMIP6 climate models capture this radiative heating by clouds within the atmosphere. While we find large differences among models, especially in cold regions of the atmosphere with abundant ice clouds, we also demonstrate that physical understanding allows us to predict the response of clouds and their radiative heating near the tropopause to climate change.
Sylvia Sullivan, Behrooz Keshtgar, Nicole Albern, Elzina Bala, Christoph Braun, Anubhav Choudhary, Johannes Hörner, Hilke Lentink, Georgios Papavasileiou, and Aiko Voigt
Geosci. Model Dev., 16, 3535–3551, https://doi.org/10.5194/gmd-16-3535-2023, https://doi.org/10.5194/gmd-16-3535-2023, 2023
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Clouds absorb and re-emit infrared radiation from Earth's surface and absorb and reflect incoming solar radiation. As a result, they change atmospheric temperature gradients that drive large-scale circulation. To better simulate this circulation, we study how the radiative heating and cooling from clouds depends on model settings like grid spacing; whether we describe convection approximately or exactly; and the level of detail used to describe small-scale processes, or microphysics, in clouds.
Fayçal Lamraoui, Martina Krämer, Armin Afchine, Adam B. Sokol, Sergey Khaykin, Apoorva Pandey, and Zhiming Kuang
Atmos. Chem. Phys., 23, 2393–2419, https://doi.org/10.5194/acp-23-2393-2023, https://doi.org/10.5194/acp-23-2393-2023, 2023
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Cirrus in the tropical tropopause layer (TTL) can play a key role in vertical transport. We investigate the role of different cloud regimes and the associated ice habits in regulating the properties of the TTL. We use high-resolution numerical experiments at the scales of large-eddy simulations (LESs) and aircraft measurements. We found that LES-scale parameterizations that predict ice shape are crucial for an accurate representation of TTL cirrus and thus the associated (de)hydration process.
Bernd Kärcher and Claudia Marcolli
Atmos. Chem. Phys., 21, 15213–15220, https://doi.org/10.5194/acp-21-15213-2021, https://doi.org/10.5194/acp-21-15213-2021, 2021
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Aerosol–cloud interactions play an important role in climate change. Simulations of the competition between homogeneous solution droplet freezing and heterogeneous ice nucleation can be compromised by the misapplication of ice-active particle fractions frequently derived from laboratory measurements or parametrizations. Our study frames the problem and establishes a solution that is easy to implement in cloud models.
Claudia Marcolli, Fabian Mahrt, and Bernd Kärcher
Atmos. Chem. Phys., 21, 7791–7843, https://doi.org/10.5194/acp-21-7791-2021, https://doi.org/10.5194/acp-21-7791-2021, 2021
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Pores are aerosol particle features that trigger ice nucleation, as they take up water by capillary condensation below water saturation that freezes at low temperatures. The pore ice can then grow into macroscopic ice crystals making up cirrus clouds. Here, we investigate the pores in soot aggregates responsible for pore condensation and freezing (PCF). Moreover, we present a framework to parameterize soot PCF that is able to predict the ice nucleation activity based on soot properties.
Sara Bacer, Sylvia C. Sullivan, Odran Sourdeval, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Atmos. Chem. Phys., 21, 1485–1505, https://doi.org/10.5194/acp-21-1485-2021, https://doi.org/10.5194/acp-21-1485-2021, 2021
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We investigate the relative importance of the rates of both microphysical processes and unphysical correction terms that act as sources or sinks of ice crystals in cold clouds. By means of numerical simulations performed with a global chemistry–climate model, we assess the relevance of these rates at global and regional scales. This estimation is of fundamental importance to assign priority to the development of microphysics parameterizations and compare model output with observations.
Martina Krämer, Christian Rolf, Nicole Spelten, Armin Afchine, David Fahey, Eric Jensen, Sergey Khaykin, Thomas Kuhn, Paul Lawson, Alexey Lykov, Laura L. Pan, Martin Riese, Andrew Rollins, Fred Stroh, Troy Thornberry, Veronika Wolf, Sarah Woods, Peter Spichtinger, Johannes Quaas, and Odran Sourdeval
Atmos. Chem. Phys., 20, 12569–12608, https://doi.org/10.5194/acp-20-12569-2020, https://doi.org/10.5194/acp-20-12569-2020, 2020
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To improve the representations of cirrus clouds in climate predictions, extended knowledge of their properties and geographical distribution is required. This study presents extensive airborne in situ and satellite remote sensing climatologies of cirrus and humidity, which serve as a guide to cirrus clouds. Further, exemplary radiative characteristics of cirrus types and also in situ observations of tropical tropopause layer cirrus and humidity in the Asian monsoon anticyclone are shown.
Wandi Yu, Andrew E. Dessler, Mijeong Park, and Eric J. Jensen
Atmos. Chem. Phys., 20, 12153–12161, https://doi.org/10.5194/acp-20-12153-2020, https://doi.org/10.5194/acp-20-12153-2020, 2020
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The stratospheric water vapor mixing ratio over North America (NA) region is up to ~ 1 ppmv higher when deep convection occurs. We find substantial consistency in the interannual variations of NA water vapor anomaly and deep convection and explain both the summer seasonal cycle and interannual variability of the convective moistening efficiency. We show that the NA anticyclone and tropical upper tropospheric temperature determine how much deep convection moistens the lower stratosphere.
Georgia Sotiropoulou, Sylvia Sullivan, Julien Savre, Gary Lloyd, Thomas Lachlan-Cope, Annica M. L. Ekman, and Athanasios Nenes
Atmos. Chem. Phys., 20, 1301–1316, https://doi.org/10.5194/acp-20-1301-2020, https://doi.org/10.5194/acp-20-1301-2020, 2020
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Arctic clouds constitute a large source of uncertainty in predictions of future climate. Observations indicate that the number concentration of cloud ice crystals exceeds the concentration of aerosols that can act as ice-nucleating particles (INPs). We show that ice multiplication due to mechanical break-up upon collisions between the few primary ice crystals (formed from INPs) can explain the discrepancy. Including a description of the process in climate models can improve cloud representation.
Lei Gu, Jie Chen, Jiabo Yin, Sylvia C. Sullivan, Hui-Min Wang, Shenglian Guo, Liping Zhang, and Jong-Suk Kim
Hydrol. Earth Syst. Sci., 24, 451–472, https://doi.org/10.5194/hess-24-451-2020, https://doi.org/10.5194/hess-24-451-2020, 2020
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Focusing on the multifaceted nature of droughts, this study quantifies the change in global drought risks for 1.5 and 2.0 °C warming trajectories by a multi-model ensemble under three representative concentration pathways (RCP2.6, 4.5 and 8.5). Socioeconomic exposures are investigated by incorporating the dynamic shared socioeconomic pathways (SSPs) into the drought impact assessment. The results show that even the ambitious 1.5 °C warming level can cause substantial increases on the global scale.
Suvarna Fadnavis, Rolf Müller, Gayatry Kalita, Matthew Rowlinson, Alexandru Rap, Jui-Lin Frank Li, Blaž Gasparini, and Anton Laakso
Atmos. Chem. Phys., 19, 9989–10008, https://doi.org/10.5194/acp-19-9989-2019, https://doi.org/10.5194/acp-19-9989-2019, 2019
Short summary
Short summary
This paper highlights the impact of Asian anthropogenic emission changes in SO2 on sulfate loading in the Asian upper troposphere–lower stratosphere from a global chemistry–climate model and satellite remote sensing. Estimated seasonal mean direct radiative forcing at the top of the atmosphere induced by the increase in Indian SO2 is −0.2–−1.5 W m2 over India. Chinese SO2 emission reduction leads to a positive radiative forcing of ~0.6–6 W m2 over China. It will likely decrease Indian rainfall.
Sylvia C. Sullivan, Christian Barthlott, Jonathan Crosier, Ilya Zhukov, Athanasios Nenes, and Corinna Hoose
Atmos. Chem. Phys., 18, 16461–16480, https://doi.org/10.5194/acp-18-16461-2018, https://doi.org/10.5194/acp-18-16461-2018, 2018
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Ice crystal formation in clouds can occur via thermodynamic nucleation, but also via mechanical collisions between pre-existing crystals or co-existing droplets. When descriptions of this mechanical ice generation are implemented into the COSMO weather model, we find that the contributions to crystal number from thermodynamic and mechanical processes are of the same order. Mechanical ice generation also intensifies differences in precipitation intensity between dynamic and quiescent regions.
Sara Bacer, Sylvia C. Sullivan, Vlassis A. Karydis, Donifan Barahona, Martina Krämer, Athanasios Nenes, Holger Tost, Alexandra P. Tsimpidi, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 11, 4021–4041, https://doi.org/10.5194/gmd-11-4021-2018, https://doi.org/10.5194/gmd-11-4021-2018, 2018
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The complexity of ice nucleation mechanisms and aerosol--ice interactions makes their representation still challenging in atmospheric models. We have implemented a comprehensive ice crystal formation parameterization in the global chemistry-climate model EMAC to improve the representation of ice crystal number concentrations. The newly implemented parameterization takes into account processes which were previously neglected by the standard version of the model.
Aurélien Podglajen, Riwal Plougonven, Albert Hertzog, and Eric Jensen
Atmos. Chem. Phys., 18, 10799–10823, https://doi.org/10.5194/acp-18-10799-2018, https://doi.org/10.5194/acp-18-10799-2018, 2018
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Using a simplified analytical setup, we show that the temperature and wind fluctuations due to an atmospheric gravity wave can induce a localization of ice crystals in a specific region of the wave. In that region, the air is nearly saturated and the vertical wind anomaly is positive. As a consequence, reversible gravity wave motions have an irreversible impact (mean upward motion) on the ice crystals. Our findings are consistent with observations of cirrus clouds near the tropical tropopause.
Daniel T. McCoy, Frida A.-M. Bender, Daniel P. Grosvenor, Johannes K. Mohrmann, Dennis L. Hartmann, Robert Wood, and Paul R. Field
Atmos. Chem. Phys., 18, 2035–2047, https://doi.org/10.5194/acp-18-2035-2018, https://doi.org/10.5194/acp-18-2035-2018, 2018
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The interaction between clouds and aerosols represents the largest source of uncertainty in the anthropogenic radiative forcing. Cloud droplet number concentration (CDNC) is the state variable that moderates the interaction between aerosol and clouds. Here we show that CDNC decreases off the coasts of East Asia and North America due to controls on emissions. We support this analysis through an examination of volcanism in Hawaii and Vanuatu.
Sylvia C. Sullivan, Corinna Hoose, Alexei Kiselev, Thomas Leisner, and Athanasios Nenes
Atmos. Chem. Phys., 18, 1593–1610, https://doi.org/10.5194/acp-18-1593-2018, https://doi.org/10.5194/acp-18-1593-2018, 2018
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Ice multiplication (IM) processes can have a profound impact on cloud and precipitation development but are poorly understood. Here we study whether a lower limit of ice nuclei exists to initiate IM. The lower limit is found to be extremely low (0.01 per liter or less). A counterintuitive but profound conclusion thus emerges: IM requires cloud formation around a thermodynamic
sweet spotand is sensitive to fluctuations in cloud condensation nuclei concentration alone.
Suvarna Fadnavis, Gayatry Kalita, K. Ravi Kumar, Blaž Gasparini, and Jui-Lin Frank Li
Atmos. Chem. Phys., 17, 11637–11654, https://doi.org/10.5194/acp-17-11637-2017, https://doi.org/10.5194/acp-17-11637-2017, 2017
Short summary
Short summary
In this study, the model simulations show that monsoon convection over the Bay of Bengal, the South China Sea and southern flanks of the Himalayas transports Asian carbonaceous aerosol into the UTLS. Carbonaceous aerosol induces enhancement in heating rate, vertical velocity and water vapor transport in the UTLS. Doubling of carbonaceous aerosols creates an anomalous warming over the TP. It generates monsoon Hadley circulation and thus increases precipitation over India and northeast China.
Blaž Gasparini, Steffen Münch, Laure Poncet, Monika Feldmann, and Ulrike Lohmann
Atmos. Chem. Phys., 17, 4871–4885, https://doi.org/10.5194/acp-17-4871-2017, https://doi.org/10.5194/acp-17-4871-2017, 2017
Short summary
Short summary
Cirrus clouds have, unlike other cloud types, a warming impact on climate. Decreasing their frequency therefore leads to a cooling effect. Cirrus ice crystals grow larger when formed on solid aerosols, inducing a shorter cloud lifetime.
We compare simplified simulations of stripping cirrus out of the sky with simulations of seeding by aerosol injections. While we find the surface climate responses to be similar, the changes in clouds and cloud properties differ significantly.
M. Kuebbeler, U. Lohmann, J. Hendricks, and B. Kärcher
Atmos. Chem. Phys., 14, 3027–3046, https://doi.org/10.5194/acp-14-3027-2014, https://doi.org/10.5194/acp-14-3027-2014, 2014
K. J. Baustian, M. E. Wise, E. J. Jensen, G. P. Schill, M. A. Freedman, and M. A. Tolbert
Atmos. Chem. Phys., 13, 5615–5628, https://doi.org/10.5194/acp-13-5615-2013, https://doi.org/10.5194/acp-13-5615-2013, 2013
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
Evaluating the Wegener-Bergeron-Findeisen process in ICON in large-eddy mode with in situ observations from the CLOUDLAB project
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
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
Aerosol-Induced Closure of Marine Cloud Cells: Enhanced Effects in the Presence of Precipitation
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
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.
Nadja Omanovic, Sylvaine Ferrachat, Christopher Fuchs, Jan Henneberger, Anna J. Miller, Kevin Ohneiser, Fabiola Ramelli, Patric Seifert, Robert Spirig, Huiying Zhang, and Ulrike Lohmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-3029, https://doi.org/10.5194/egusphere-2023-3029, 2024
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We present simulations with a high-resolution numerical weather prediction model to study the growth of ice crystals in low clouds following glaciogenic seeding. We show that the simulated ice crystals grow slower than observed and do not consume as many cloud droplets as measured in the field. This may have implications for forecasting precipitation as the ice phase is crucial for precipitation in mid- and high latitudes.
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.
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.
Matthew W. Christensen, Peng Wu, Adam C. Varble, Heng Xiao, and Jerome D. Fast
EGUsphere, https://doi.org/10.5194/egusphere-2023-2416, https://doi.org/10.5194/egusphere-2023-2416, 2023
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Clouds are essential to keep Earth cooler by reflecting sunlight back to space. We show that an increase in aerosol concentration suppresses precipitation in clouds, causing them to accumulate water and expand in a polluted environment with stronger turbulence and radiative cooling. This process enhances their reflectance by 51 %. It’s therefore prudent to account for cloud fraction changes in assessments of aerosol-cloud interactions to improve predictions of climate change.
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.
Cited articles
Ackerman, T. P., Liou, K.-N., Valero, P. J. F., and Pfister, L.: Heating Rates in Tropical Anvils, J. Atmos. Sci., 45, 1606–1623, https://doi.org/10.1175/1520-0469(1988)045<1606:HRITA>2.0.CO;2, 1988. a, b, c
Albern, N., Voigt, A., Buehler, S. A., and Grützun, V.: Robust and Nonrobust Impacts of Atmospheric Cloud-Radiative Interactions on the Tropical Circulation and Its Response to Surface Warming, Geophys. Res. Lett., 45, 8577–8585, https://doi.org/10.1029/2018GL079599, 2018. a, b
Alexander, M. J. and Pfister, L.: Gravity wave momentum flux in the lower stratosphere over convection, Geophys. Res. Lett., 22, 2029–2032, https://doi.org/10.1029/95GL01984, 1995. a
Amell, A., Eriksson, P., and Pfreundschuh, S.: Ice water path retrievals from Meteosat-9 using quantile regression neural networks, Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, 2022. a
Atkinson, J. D., Murray, B. J., Woodhouse, M. T., Whale, T. F., Baustian, K. J., Carslaw, K. S., Dobbie, S., O'Sullivan, D., and Malkin, T. L.: The importance of feldspar for ice nucleation by mineral dust in mixed-phase clouds., Nature, 498, 355–358, https://doi.org/10.1038/nature12278, 2013. a
Atlas, R. and Bretherton, C. S.: Aircraft observations of gravity wave activity and turbulence in the tropical tropopause layer: prevalence, influence on cirrus clouds, and comparison with global storm-resolving models, Atmos. Chem. Phys., 23, 4009–4030, https://doi.org/10.5194/acp-23-4009-2023, 2023. a, b, c
Atlas,R., Bretherton, C.S., Sokol., A., Blossey, P., and Khairoutdinov, M.: What are the causes of tropical cirrus longwave biases in global storm resolving simulations?, Earth Space Sci. Open Arch., [preprint], https://doi.org/10.1002/essoar.10511104.1, 13 April 2022. a
Avery, M., Winker, D., Heymsfield, A., Vaughan, M., Young, S., Hu, Y., and Trepte, C.: Cloud ice water content retrieved from the CALIOP space-based lidar, Geophys. Res. Lett., 39, 2–7, https://doi.org/10.1029/2011GL050545, 2012. a
Bacer, S., Sullivan, S. C., Sourdeval, O., Tost, H., Lelieveld, J., and Pozzer, A.: Cold cloud microphysical process rates in a global chemistry–climate model, Atmos. Chem. Phys., 21, 1485–1505, https://doi.org/10.5194/acp-21-1485-2021, 2021. a
Barahona, D. and Nenes, A.: Parameterization of cirrus cloud formation in large-scale models: Homogeneous nucleation, J. Geophys. Res.-Atmos., 113, D11211, https://doi.org/10.1029/2007JD009355, 2008. a
Barahona, D. and Nenes, A.: Parameterizing the competition between homogeneous and heterogeneous freezing in cirrus cloud formation – monodisperse ice nuclei, Atmos. Chem. Phys., 9, 369–381, https://doi.org/10.5194/acp-9-369-2009, 2009. a
Barahona, D., Molod, A., and Kalesse, H.: Direct estimation of the global distribution of vertical velocity within cirrus clouds, Sci. Rep., 7, 6840, https://doi.org/10.1038/s41598-017-07038-6, 2017. a
Baran, A. J., Hill, P., Furtado, K., Field, P., and Manners, J.: A Coupled Cloud Physics–radiation Parameterization of the Bulk Optical Properties of Cirrus and Its Impact on the Met Office Unified Model Global Atmosphere 5.0 Configuration, J. Climate, 27, 7725–7752, https://doi.org/10.1175/JCLI-D-13-00700.1, 2014. a
Baran, A. J., Hill, P., Walters, D., Hardiman, S. C., Furtado, K., Field, P. R., and Manners, J.: The Impact of Two Coupled Cirrus Microphysics–Radiation Parameterizations on the Temperature and Specific Humidity Biases in the Tropical Tropopause Layer in a Climate Model, J. Climate, 29, 5299–5316, https://doi.org/10.1175/JCLI-D-15-0821.1, 2016. a
Barber, K. A., Mullendore, G. L., and Alexander, M. J.: Out-of-Cloud Convective Turbulence: Estimation Method and Impacts of Model Resolution, J. Appl. Meteorol. Climatol., 57, 121–136, https://doi.org/10.1175/JAMC-D-17-0174.1, 2018. a
Bartolomé García, I., Sourdeval, O., Spang, R., and Krämer, M.: Technical note: Bimodal Parameterizations of in situ Ice Cloud Particle Size Distributions, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-754, 2023. a
Behrangi, A., Kubar, T., and Lambrigtsen, B.: Phenomenological Description of Tropical Clouds Using CloudSat Cloud Classification, Mon. Weather Rev., 140, 3235–3249, https://doi.org/10.1175/MWR-D-11-00247.1, 2012. a
Betts, A. K. and Ridgway, W.: Climatic Equilibrium of the Atmospheric Convective Boundary Layer over a Tropical Ocean, J. Atmos. Sci., 46, 2621–2641, https://doi.org/10.1175/1520-0469(1989)046<2621:CEOTAC>2.0.CO;2, 1989. a
Beucler, T., Ebert-Uphoff, I., Rasp, S., Pritchard, M., and Gentine, P.: Machine Learing for Clouds and Climate, in: Clouds and Their Climatic Impact: Radiation, Circulation, and Precipitation, edited by: Sullivan, S. C. and Hoose, C., Wiley–American Geophysical Union, 327–346, ISBN 978-1-119-70031-9, 2024. a
Beydoun, H., Caldwell, P. M., Hannah, W. M., and Donahue, A. S.: Dissecting Anvil Cloud Response to Sea Surface Warming, Geophys. Res. Lett., 48, e2021GL094049, https://doi.org/10.1029/2021GL094049, 2021. a
Blossey, P. N., Kuang, Z., and Romps, D. M.: Isotopic composition of water in the tropical tropopause layer in cloud-resolving simulations of an idealized tropical circulation, J. Geophys. Res.-Atmos., 115, 1–23, https://doi.org/10.1029/2010JD014554, 2010. a
Boehm, M. T. and Verlinde, J.: Stratospheric influence on upper tropospheric tropical cirrus, Geophys. Res. Lett., 27, 3209–3212, https://doi.org/10.1029/2000GL011678, 2000. a
Bony, S., Stevens, B., Coppin, D., Becker, T., Reed, K. A., Voigt, A., and Medeiros, B.: Thermodynamic control of anvil cloud amount, P. Natl. Acad. Sci. USA, 113, 8927–8932, https://doi.org/10.1073/pnas.1601472113, 2016. a, b
Bony, S., Semie, A., Kramer, R. J., Soden, B., Tompkins, A. M., and Emanuel, K. A.: Observed Modulation of the Tropical Radiation Budget by Deep Convective Organization and Lower-Tropospheric Stability, AGU Advances, 1, e2019AV000155, https://doi.org/10.1029/2019AV000155, 2020. a
Bouniol, D., Roca, R., Fiolleau, T., and Poan, D. E.: Macrophysical, Microphysical, and Radiative Properties of Tropical Mesoscale Convective Systems over Their Life Cycle, J. Climate, 29, 3353–3371, https://doi.org/10.1175/JCLI-D-15-0551.1, 2016. a
Bramberger, M., Alexander, M. J., Davis, S., Podglajen, A., Hertzog, A., Kalnajs, L., Deshler, T., Goetz, J. D., and Khaykin, S.: First Super-Pressure Balloon-Borne Fine-Vertical-Scale Profiles in the Upper TTL: Impacts of Atmospheric Waves on Cirrus Clouds and the QBO, Geophys. Res. Lett., 49, e2021GL097596, https://doi.org/10.1029/2021GL097596, 2022. a
Braun, S. A., Yorks, J., Thorsen, T., Cecil, D., and Kirschbaum, D.: NASA'S Earth System Observatory-Atmosphere Observing System, in: IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, 7391–7393, https://doi.org/10.1109/igarss46834.2022.9884029, 2022. a
Caldwell, P. M., Terai, C. R., Hillman, B., Keen, N. D., Bogenschutz, P., Lin, W., Beydoun, H., Taylor, M., Bertagna, L., Bradley, A. M., Clevenger, T. C., Donahue, A. S., Eldred, C., Foucar, J., Golaz, J.-C., Guba, O., Jacob, R., Johnson, J., Krishna, J., Liu, W., Pressel, K., Salinger, A. G., Singh, B., Steyer, A., Ullrich, P., Wu, D., Yuan, X., Shpund, J., Ma, H.-Y., and Zender, C. S.: Convection-Permitting Simulations With the E3SM Global Atmosphere Model, J. Adv. Model. Earth Syst., 13, e2021MS002544, https://doi.org/10.1029/2021MS002544, 2021. a
Cesana, G., Waliser, D. E., Henderson, D., L'Ecuyer, T. S., Jiang, X., and Li, J.-L. F.: The Vertical Structure of Radiative Heating Rates: A Multimodel Evaluation Using A-Train Satellite Observations, J. Climate, 32, 1573–1590, https://doi.org/10.1175/JCLI-D-17-0136.1, 2019. a
Chahine, M. T., Pagano, T. S., Aumann, H. H., Atlas, R., Barnet, C., Blaisdell, J., Chen, L., Divakarla, M., Fetzer, E. J., Goldberg, M., Gautier, C., Granger, S., Hannon, S., Irion, F. W., Kakar, R., Kalnay, E., Lambrigtsen, B. H., Lee, S.-Y., Marshall, J. L., McMillan, W. W., McMillin, L., Olsen, E. T., Revercomb, H., Rosenkranz, P., Smith, W. L., Staelin, D., Strow, L. L., Susskind, J., Tobin, D., Wolf, W., and Zhou, L.: AIRS: Improving Weather Forecasting and Providing New Data on Greenhouse Gases, B. Am. Meteorol. Soc., 87, 911–926, https://doi.org/10.1175/BAMS-87-7-911, 2006. a
Chambers, L. H., Lin, B., and Young, D. F.: Examination of New CERES Data for Evidence of Tropical Iris Feedback, J. Climate, 15, 3719–3726, https://doi.org/10.1175/1520-0442(2002)015<3719:EONCDF>2.0.CO;2, 2002. a
Chang, K.-W. and L'Ecuyer, T.: Influence of gravity wave temperature anomalies and their vertical gradients on cirrus clouds in the tropical tropopause layer – a satellite-based view, Atmos. Chem. Phys., 20, 12499–12514, https://doi.org/10.5194/acp-20-12499-2020, 2020. a
Choi, Y.-S., Kim, W., Yeh, S.-W., Masunaga, H., Kwon, M.-J., Jo, H.-S., and Huang, L.: Revisiting the iris effect of tropical cirrus clouds with TRMM and A-Train satellite data, J. Geophys. Res.-Atmos., 122, 5917–5931, https://doi.org/10.1002/2016JD025827, 2017. a, b, c
Cirisan, A., Luo, B. P., Engel, I., Wienhold, F. G., Sprenger, M., Krieger, U. K., Weers, U., Romanens, G., Levrat, G., Jeannet, P., Ruffieux, D., Philipona, R., Calpini, B., Spichtinger, P., and Peter, T.: Balloon-borne match measurements of midlatitude cirrus clouds, Atmos. Chem. Phys., 14, 7341–7365, https://doi.org/10.5194/acp-14-7341-2014, 2014. a
Connolly, P. J., Emersic, C., and Field, P. R.: A laboratory investigation into the aggregation efficiency of small ice crystals, Atmos. Chem. Phys., 12, 2055–2076, https://doi.org/10.5194/acp-12-2055-2012, 2012. a
Corcos, M., Hertzog, A., Plougonven, R., and Podglajen, A.: Observation of gravity waves at the tropical tropopause using superpressure balloons., J. Geophys. Res., 126, e2021JD035165, https://doi.org/10.1029/2021JD035165, 2021. a, b
Corcos, M., Hertzog, A., Plougonven, R., and Podglajen, A.: A simple model to assess the impact of gravity waves on ice-crystal populations in the tropical tropopause layer, Atmos. Chem. Phys., 23, 6923–6939, https://doi.org/10.5194/acp-23-6923-2023, 2023. a
David, R. O., Marcolli, C., Fahrni, J., Qiu, Y., Sirkin, Y. A. P., Molinero, V., Mahrt, F., Brühwiler, D., Lohmann, U., and Kanji, Z. A.: Pore condensation and freezing is responsible for ice formation below water saturation for porous particles, P. Natl. Acad. Sci. USA, 116, 8184–8189, https://doi.org/10.1073/pnas.1813647116, 2019. a
Davis, S. M., Hlavka, D., Jensen, E., Rosenlof, K., Yang, Q., Schmidt, S., Borrmann, S., Frey, W., Lawson, P., Vömel, H., and Bui, T.-P.: In situ and lidar observations of tropopause subvisible cirrus clouds during TC4, J. Geophys. Res., 115, D00J17, https://doi.org/10.1029/2009JD013093, 2010. a
Davis, S. M., Liang, C. K., and Rosenlof, K.: Interannual variability of tropical tropopause layer clouds, Geophys. Res. Lett., 40, 2862–2866, https://doi.org/10.1002/grl.50512, 2013. a
Delanoë, J. and Hogan, R. J.: Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds, J. Geophys. Res., 115, 1–17, https://doi.org/10.1029/2009JD012346, 2010. a
Delanoë, J. M. and Hogan, R. J.: A variational scheme for retrieving ice cloud properties from combined radar, lidar, and infrared radiometer, J. Geophys. Res.-Atmos., 113, 1–21, https://doi.org/10.1029/2007JD009000, 2008. a
DeMott, P. J., Cziczo, D. J., Prenni, A. J., Murphy, D. M., Kreidenweis, S. M., Thomson, D. S., Borys, R., and Rogers, D. C.: Measurements of the concentration and composition of nuclei for cirrus formation, P. Natl. Acad. Sci. USA, 100, 14655–14660, https://doi.org/10.1073/pnas.2532677100, 2003. a
Deng, M. and Mace, G. G.: Cirrus microphysical properties and air motion statistics using cloud radar doppler moments. Part II: Climatology, J. Appl. Meteorol. Climatol., 47, 3221–3235, https://doi.org/10.1175/2008JAMC1949.1, 2008. a
Deng, M., Mace, G. G., Wang, Z., and Okamoto, H.: Tropical composition, cloud and climate coupling experiment validation for cirrus cloud profiling retrieval using cloudsat radar and CALIPSO lidar, J. Geophys. Res.-Atmos., 115, 1–18, https://doi.org/10.1029/2009JD013104, 2010. a
de Vries, A. J., Aemisegger, F., Pfahl, S., and Wernli, H.: Stable water isotope signals in tropical ice clouds in the West African monsoon simulated with a regional convection-permitting model, Atmos. Chem. Phys., 22, 8863–8895, https://doi.org/10.5194/acp-22-8863-2022, 2022. a
Dietlicher, R., Neubauer, D., and Lohmann, U.: Elucidating ice formation pathways in the aerosol–climate model ECHAM6-HAM2, Atmos. Chem. Phys., 19, 9061–9080, https://doi.org/10.5194/acp-19-9061-2019, 2019. a
Dinh, T., Podglajen, A., Hertzog, A., Legras, B., and Plougonven, R.: Effect of gravity wave temperature fluctuations on homogeneous ice nucleation in the tropical tropopause layer, Atmos. Chem. Phys., 16, 35–46, https://doi.org/10.5194/acp-16-35-2016, 2016. a
Dinh, T. P., Durran, D. R., and Ackerman, T. P.: Maintenance of tropical tropopause layer cirrus, J. Geophys. Res.-Atmos., 115, 1–15, https://doi.org/10.1029/2009JD012735, 2010. a, b, c
Dobbie, S. and Jonas, P.: Radiative influences on the structure and lifetime of cirrus clouds, Q. J. Roy. Meteorol. Soc., 127, 2663–2682, https://doi.org/10.1002/qj.49712757808, 2001. a
Donner, L. J., O'Brien, T. A., Rieger, D., Vogel, B., and Cooke, W. F.: Are atmospheric updrafts a key to unlocking climate forcing and sensitivity?, Atmos. Chem. Phys., 16, 12983–12992, https://doi.org/10.5194/acp-16-12983-2016, 2016. a
Durran, D. R., Dinh, T., Ammerman, M., and Ackerman, T.: The Mesoscale Dynamics of Thin Tropical Tropopause Cirrus, J. Atmos. Sci., 66, 2859–2873, https://doi.org/10.1175/2009jas3046.1, 2009. a
Hawker, R. E., Miltenberger, A. K., Wilkinson, J. M., Hill, A. A., Shipway, B. J., Cui, Z., Cotton, R. J., Carslaw, K. S., Field, P. R., and Murray, B. J.: The temperature dependence of ice-nucleating particle concentrations affects the radiative properties of tropical convective cloud systems, Atmos. Chem. Phys., 21, 5439–5461, https://doi.org/10.5194/acp-21-5439-2021, 2021. a
Eriksson, P., Rydberg, B., Mattioli, V., Thoss, A., Accadia, C., Klein, U., and Buehler, S. A.: Towards an operational Ice Cloud Imager (ICI) retrieval product, Atmos. Meas. Tech., 13, 53–71, https://doi.org/10.5194/amt-13-53-2020, 2020. a
Feng, Z., Dong, X., Xi, B., Schumacher, C., Minnis, P., and Khaiyer, M.: Top-of-atmosphere radiation budget of convective core/stratiform rain and anvil clouds from deep convective systems, J. Geophys. Res., D23202,, https://doi.org/10.1029/2011JD016451, 2011. a
Feng, Z., Leung, L. R., Liu, N., Wang, J., Houze Jr, R. A., Li, J., Hardin, J. C., Chen, D., and Guo, J.: A Global High-Resolution Mesoscale Convective System Database Using Satellite-Derived Cloud Tops, Surface Precipitation, and Tracking, J. Geophys. Res.-Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202, 2021. a
Feng, Z., Leung, L. R., Hardin, J., Terai, C. R., Song, F., and Caldwell, P.: Mesoscale Convective Systems in DYAMOND Global Convection-Permitting Simulations, Geophys. Res. Lett., 50, e2022GL102603, https://doi.org/10.1029/2022GL102603, 2023. a, b
Ferlay, N., Garrett, T. J., and Minvielle, F.: Satellite Observations of an Unusual Cloud Formation near the Tropopause, J. Atmos. Sci., 71, 3801–3815, https://doi.org/10.1175/jas-d-13-0361.1, 2014. a
Fiolleau, T. and Roca, R.: An algorithm for the detection and tracking of tropical mesoscale convective systems using infrared images from geostationary satellite, IEEE Trans. Geosci. Remote Sens., 51, 4302–4315, https://doi.org/10.1109/TGRS.2012.2227762, 2013. a, b, c
Forster, P. M. F. and Shine, K. P.: Assessing the climate impact of trends in stratospheric water vapor, Geophys. Res. Lett., 29, 10–1/4, https://doi.org/10.1029/2001GL013909, 2002. a
Fougnie, B., Marbach, T., Lacan, A., Lang, R., Schlüssel, P., Poli, G., Munro, R., and Couto, A. B.: The Multi-Viewing Multi-Channel Multi-Polarisation Imager – Overview of the 3mi Polarimetric Mission for Aerosol and Cloud Characterization, J. Quant. Spectrosc. Ra., 219, 23–32, https://doi.org/10.1016/j.jqsrt.2018.07.008, 2018. a
Frey, W., Borrmann, S., Kunkel, D., Weigel, R., de Reus, M., Schlager, H., Roiger, A., Voigt, C., Hoor, P., Curtius, J., Krämer, M., Schiller, C., Volk, C. M., Homan, C. D., Fierli, F., Di Donfrancesco, G., Ulanovsky, A., Ravegnani, F., Sitnikov, N. M., Viciani, S., D'Amato, F., Shur, G. N., Belyaev, G. V., Law, K. S., and Cairo, F.: In situ measurements of tropical cloud properties in the West African Monsoon: upper tropospheric ice clouds, Mesoscale Convective System outflow, and subvisual cirrus, Atmos. Chem. Phys., 11, 5569–5590, https://doi.org/10.5194/acp-11-5569-2011, 2011. a
Froyd, K. D., Yu, P., Schill, G. P., Brock, C. A., Kupc, A., Williamson, C. J., Jensen, E. J., Ray, E., Rosenlof, K. H., Bian, H., Darmenov, A. S., Colarco, P. R., Diskin, G. S., Bui, T. P., and Murphy, D. M.: Dominant role of mineral dust in cirrus cloud formation revealed by global-scale measurements, Nat. Geosci., 15, 177–183, https://doi.org/10.1038/s41561-022-00901-w, 2022. a, b, c
Fu, Q.: Bottom up in the tropics, Nat. Clim. Change, 3, 957–958, https://doi.org/10.1038/nclimate2039, 2013. a
Fu, Q., Baker, M., and Hartmann, D. L.: Tropical cirrus and water vapor: an effective Earth infrared iris feedback?, Atmos. Chem. Phys., 2, 31–37, https://doi.org/10.5194/acp-2-31-2002, 2002. a
Fu, Q., Smith, M., and Yang, Q.: The impact of cloud radiative effects on the tropical tropopause layer temperatures, Atmosphere, 9, 1–13, https://doi.org/10.3390/atmos9100377, 2018. a, b
Fueglistaler, S., Dessler, A. E., Dunkerton, T. J., Folkins, I., Fu, Q., and Mote, P. W.: Tropical tropopause layer, Rev. Geophys., 47, 1–31, https://doi.org/10.1029/2008RG000267, 2009. a
Galewsky, J., Steen-Larsen, H. C., Field, R. D., Worden, J., Risi, C., and Schneider, M.: Stable isotopes in atmospheric water vapor and applications to the hydrologic cycle, Rev. Geophys., 54, 809–865, https://doi.org/10.1002/2015RG000512, 2016. a
Garrett, T. J., Heymsfield, A. J., McGill, M. J., Ridley, B. A., Baumgardner, D. G., Bui, T. P., and Webster, C. R.: Convective generation of cirrus near the tropopause, J. Geophys. Res.-Atmos., 109, D21203, https://doi.org/10.1029/2004JD004952, 2004. a
Garrett, T. J., Navarro, B. C., Twohy, C. H., Jensen, E. J., Baumgardner, D. G., Bui, P. T., Gerber, H., Herman, R. L., Heymsfield, A. J., Lawson, P., Minnis, P., Nguyen, L., Poellot, M., Pope, S. K., Valero, F. P., and Weinstock, E. M.: Evolution of a Florida cirrus anvil, J. Atmos. Sci., 62, 2352–2372, https://doi.org/10.1175/JAS3495.1, 2005. a
Gasparini, B., Rasch, P. J., Hartmann, D. L., Wall, C. J., and Dütsch, M.: A Lagrangian perspective on tropical anvil cloud lifecycle in present and future climate, J. Geophys. Res.-Atmos., 126, 1–26, https://doi.org/10.1029/2020jd033487, 2021. a, b, c
Gasparini, B., Sokol, A. B., Wall, C. J., Hartmann, D. L., and Blossey, P. N.: Diurnal Differences in Tropical Maritime Anvil Cloud Evolution, J. Climate, 35, 1655–1677, https://doi.org/10.1175/jcli-d-21-0211.1, 2022. a, b, c, d
Geer, A. J., Baordo, F., Bormann, N., Chambon, P., English, S. J., Kazumori, M., Lawrence, H., Lean, P., Lonitz, K., and Lupu, C.: The growing impact of satellite observations sensitive to humidity, cloud and precipitation, Q. J. Roy. Meteorol. Soc., 143, 3189–3206, https://doi.org/10.1002/qj.3172, 2017. a
Gong, J., Zeng, X., Wu, D. L., and Li, X.: Diurnal Variation of Tropical Ice Cloud Microphysics: Evidence from Global Precipitation Measurement Microwave Imager Polarimetric Measurements, J. Geophys. Res., 45, 1185–1193, 2017. a
Grabowski, W. W., Morrison, H., Shima, S.-I., Abade, G. C., Dziekan, P., and Pawlowska, H.: Modeling of Cloud Microphysics: Can We Do Better?, B. Am. Meteorol. Soc., 100, 655–672, https://doi.org/10.1175/bams-d-18-0005.1, 2019. a
Gryspeerdt, E., Sourdeval, O., Quaas, J., Delanoë, J., Krämer, M., and Kühne, P.: Ice crystal number concentration estimates from lidar–radar satellite remote sensing – Part 2: Controls on the ice crystal number concentration, Atmos. Chem. Phys., 18, 14351–14370, https://doi.org/10.5194/acp-18-14351-2018, 2018. a, b
Haase, J. S., Alexander, M. J., Hertzog, A., Kalnajs, L., Deshler, T., Davis, S. M., Plougonven, R., Cocquerez, P., , and Venel, S.: Around the world in 84 days, Eos, Transactions American Geophysical Union, https://doi.org/10.1029/2018EO091907, 2018. a
Haladay, T. and Stephens, G.: Characteristics of tropical thin cirrus clouds deduced from joint CloudSat and CALIPSO observations, J. Geophys. Res., 114, 1–13, https://doi.org/10.1029/2008JD010675, 2009. a, b, c
Hang, Y., L’Ecuyer, T. S., Henderson, D. S., Matus, A. V., and Wang, Z.: Reassessing the Effect of Cloud Type on Earth’s Energy Balance in the Age of Active Spaceborne Observations. Part II: Atmospheric Heating, J. Climate, 32, 6219–6236, https://doi.org/10.1175/JCLI-D-18-0754.1, 2019. a
Harrop, B. E. and Hartmann, D. L.: Testing the Role of Radiation in Determining Tropical Cloud-Top Temperature, J. Climate, 25, 5731–5747, https://doi.org/10.1175/jcli-d-11-00445.1, 2012. a, b
Harrop, B. E. and Hartmann, D. L.: The Role of Cloud Radiative Heating in Determining the Location of the ITCZ in Aquaplanet Simulations, J. Climate, 29, 2741–2763, https://doi.org/10.1175/JCLI-D-15-0521.1, 2016. a
Hartmann, D., Dygert, B. D., Blossey, P. N., Fu, Q., and Sokol, A. B.: The Vertical Profile of Radiative Cooling and Lapse Rate in aWarming Climate, J. Climate, 35, 2653–2665, https://doi.org/10.1175/JCLI-D-21-0861.1, 2022. a
Hartmann, D. L. and Larson, K.: An important constraint on tropical cloud-climate feedback, Geophys. Res Lett., 29, 1951–1954, https://doi.org/10.1029/2002GL015835, 2002. a
Hartmann, D. L. and Michelsen, M. L.: No evidence for Iris, B. Am. Meteorol. Soc., 83, 249–254, https://doi.org/10.1175/1520-0477(2002)083<0249:NEFI>2.3.CO;2, 2002. a
Hartmann, D. L. and Short, D. A.: On the Use of Earth Radiation Budget Statistics for Studies of Clouds and Climate, J. Atmos. Sci., 6, 1233–1250, 1980. a
Hartmann, D. L., Moy, L. A., and Fu, Q.: Tropical convection and the energy balance at the top of the atmosphere, J. Climate, 14, 4495–4511, https://doi.org/10.1175/1520-0442(2001)014<4495:TCATEB>2.0.CO;2, 2001. a
Hartmann, D. L., Blossey, P. N., and Dygert, B. D.: Convection and Climate: What Have We Learned from Simple Models and Simplified Settings?, Current Clim. Change Rep., 5, 196–206, https://doi.org/10.1007/s40641-019-00136-9, 2019. a, b
Heikenfeld, M., White, B., Labbouz, L., and Stier, P.: Aerosol effects on deep convection: the propagation of aerosol perturbations through convective cloud microphysics, Atmos. Chem. Phys., 19, 2601–2627, https://doi.org/10.5194/acp-19-2601-2019, 2019. a
Held, I. M.: The Gap between Simulation and Understanding in Climate Modeling, B. Am. Meteorol. Soc., 86, 1609–1614, https://doi.org/10.1175/BAMS-86-11-1609, 2005. a
Held, I. M. and Soden, B. J.: Robust Responses of the Hydrological Cycle to Global Warming, J. Climate, 19, 5686–5699, https://doi.org/10.1175/JCLI3990.1, 2006. a
Henderson, D. S., L'Ecuyer, T., Stephens, G., Partain, P., and Sekiguchi, M.: A Multisensor Perspective on the Radiative Impacts of Clouds and Aerosols, J. Appl. Meteorol. Climatol., 52, 853–871, https://doi.org/10.1175/JAMC-D-12-025.1, 2013. a
Heymsfield, A. and Willis, P.: Cloud Conditions Favoring Secondary Ice Particle Production in Tropical Maritime Convection, J. Atmos. Sci., 71, 4500 – 4526, https://doi.org/10.1175/JAS-D-14-0093.1, 2014. a
Hidalgo, C. A. and Almossawi, A.: The data-visualization revolution, Sci. Am., Vol. 310, 104–113, March 2014. a
Hilton, F., Armante, R., August, T., Barnet, C., Bouchard, A., Camy-Peyret, C., Capelle, V., Clarisse, L., Clerbaux, C., Coheur, P.-F., Collard, A., Crevoisier, C., Dufour, G., Edwards, D., Faijan, F., Fourrié, N., Gambacorta, A., Goldberg, M., Guidard, V., Hurtmans, D., Illingworth, S., Jacquinet-Husson, N., Kerzenmacher, T., Klaes, D., Lavanant, L., Masiello, G., Matricardi, M., McNally, A., Newman, S., Pavelin, E., Payan, S., Péquignot, E., Peyridieu, S., Phulpin, T., Remedios, J., Schlüssel, P., Serio, C., Strow, L., Stubenrauch, C., Taylor, J., Tobin, D., Wolf, W., and Zhou, D.: Hyperspectral Earth Observation from IASI: Five Years of Accomplishments, B. Am. Meteorol. Soc., 93, 347–370, https://doi.org/10.1175/BAMS-D-11-00027.1, 2012. a
Holloway, C. E. and Woolnough, S. J.: The sensitivity of convective aggregation to diabatic processes in idealized radiative-convective equilibrium simulations, J. Adv. Model. Earth Syst., 8, 166–195, https://doi.org/10.1002/2015MS000511, 2016. a
Holloway, C. E., Wing, A. A., Bony, S., Muller, C., Masunaga, H., L’Ecuyer, T. S., Turner, D. D., and Zuidema, P.: Observing Convective Aggregation, Surv. Geophys., 38, 1199–1236, https://doi.org/10.1007/s10712-017-9419-1, 2017. a
Holton, J. R. and Gettelman, A.: Horizontal transport and the dehydration of the stratosphere, Geophys. Res. Lett., 28, 2799–2802, https://doi.org/10.1029/2001GL013148, 2001. a
Holton, J. R., Haynes, P. H., McIntyre, M. E., Douglass, A. R., Rood, R. B., and Pfister, L.: Stratosphere-troposphere exchange, Rev. Geophys., 33, 403–439, https://doi.org/10.1029/95RG02097, 1995. a
Hong, Y., Liu, G., and Li, J.-L. F.: Assessing the Radiative Effects of Global Ice Clouds Based on CloudSat and CALIPSO Measurements, J. Climate, 29, 7651–7673, https://doi.org/10.1175/JCLI-D-15-0799.1, 2016. a
Hu, Y., McFarquhar, G. M., Wu, W., Huang, Y., Schwarzenboeck, A., Protat, A., Korolev, A., Rauber, R. M., and Wang, H.: Dependence of Ice Microphysical Properties on Environmental Parameters: Results from HAIC-HIWC Cayenne Field Campaign, J. Atmos. Sci., 78, 2957 – 2981, https://doi.org/10.1175/JAS-D-21-0015.1, 2021a. a
Hu, Z., Lamraoui, F., and Kuang, Z.: Influence of Upper-Troposphere Stratification and Cloud–Radiation Interaction on Convective Overshoots in the Tropical Tropopause Layer, Journal of the Atmospheric Sciences, 78, 2493–2509, https://doi.org/10.1175/JAS-D-20-0241.1, 2021b. a
Huang, Y., Wang, Y., and Huang, H.: Stratospheric water vapor feedback disclosed by a locking experiment, Geophys. Res. Lett., 47, e2020GL087987, https://doi.org/10.1029/2020GL087987, 2020. a
Hubbard, K. G.: Parameterization of Depositional Ice Growth, J. Appl. Meteorol. Climatol., 16, 177–182, https://doi.org/10.1175/1520-0450(1977)016<0177:PODIG>2.0.CO;2, 1977. a
Höjgård-Olsen, E., Chepfer, H., and Brogniez, H.: Satellite Observed Sensitivity of Tropical Clouds and Moisture to Sea Surface Temperature on Various Time and Space Scales: 1. Focus on High Level Cloud Situations Over Ocean, J. Geophys. Res.-Atmos., 127, e2021JD035438, https://doi.org/10.1029/2021JD035438, 2022. a, b
Igel, M. R., Drager, A. J., and van den Heever, S. C.: A CloudSat cloud object partitioning technique and assessment and integration of deep convective anvil sensitivities to sea surface temperature, J. Geophys. Res.-Atmos., 119, 10515–10535, https://doi.org/10.1002/2014JD021717, 2014. a
Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H., Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P., Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota, T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki, R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez, A., Wandinger, U., Wehr, T., and van Zadelhoff, G. J.: The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation, B. Am. Meteorol. Soc, 96, 1311–1332, https://doi.org/10.1175/BAMS-D-12-00227.1, 2015. a, b
Immler, F., Krüger, K., Fujiwara, M., Verver, G., Rex, M., and Schrems, O.: Correlation between equatorial Kelvin waves and the occurrence of extremely thin ice clouds at the tropical tropopause, Atmos. Chem. Phys., 8, 4019–4026, https://doi.org/10.5194/acp-8-4019-2008, 2008. a
Ito, M. and Masunaga, H.: Process-Level Assessment of the Iris Effect Over Tropical Oceans, Geophys. Res. Lett., 49, e2022GL097997, https://doi.org/10.1029/2022GL097997, 2022. a, b
Janisková, M.: Assimilation of Cloud Information From Space‐borne Radar and Lidar: Experimental Study Using a 1d+4d‐var Technique, Quarterly J. Roy. Meteorol. Soc., 141, 2708–2725, https://doi.org/10.1002/qj.2558, 2015. a
Järvinen, E., Wernli, H., and Schnaiter, M.: Investigations of Mesoscopic Complexity of Small Ice Crystals in Midlatitude Cirrus, Geophys. Res. Lett., 45, 11,465–11,472, https://doi.org/10.1029/2018GL079079, 2018. a
Jeevanjee, N.: Three Rules for the Decrease of Tropical Convection With Global Warming, J. Adv. Model. Earth Syst., 14, e2022MS003285, https://doi.org/10.1029/2022MS003285, 2022. a, b
Jeevanjee, N. and Fueglistaler, S.: Simple Spectral Models for Atmospheric Radiative Cooling, J. Atmos. Sci., 77, 479–497, https://doi.org/10.1175/jas-d-18-0347.1, 2020. a
Jeevanjee, N. and Zhou, L.: On the Resolution-Dependence of Anvil Cloud Fraction and Precipitation Efficiency in Radiative-Convective Equilibrium, J. Adv. Model. Earth Syst., 14, e2021MS002759, https://doi.org/10.1029/2021MS002759, 2022. a, b
Jeevanjee, N., Hassanzadeh, P., Hill, S., and Sheshadri, A.: A perspective on climate model hierarchies, J. Adv. Model. Earth Syst., 9, 1760–1771, https://doi.org/10.1002/2017MS001038, 2017. a, b
Jeggle, K., Neubauer, D., Camps-Valls, G., and Lohmann, U.: Understanding cirrus clouds using explainable machine learning, Environ. Data Sci., 2, e19, https://doi.org/10.1017/eds.2023.14, 2023. a
Jenney, A. M., Randall, D. A., and Branson, M. D.: Understanding the Response of Tropical Ascent to Warming Using an Energy Balance Framework, J. Adv. Model. Earth Syst., 12, e2020MS002056, https://doi.org/10.1029/2020MS002056, 2020. a
Jensen, E. and Pfister, L.: Transport and freeze-drying in the tropical tropopause layer, J. Geophys. Res-.Atmos., 109, https://doi.org/10.1029/2003JD004022, 2004. a
Jensen, E., Starr, D., and Toon, O. B.: Mission investigates tropical cirrus clouds, Eos, Transactions American Geophysical Union, 85, 45–50, https://doi.org/10.1029/2004EO050002, 2004. a
Jensen, E. J., Kinne, S., and Toon, O. B.: Tropical cirrus cloud radiative forcing: Sensitivity studies, Geophys. Res. Lett., 21, 2023–2026, https://doi.org/10.1029/94GL01358, 1994. a
Jensen, E. J., Toon, O. B., Pfister, L., and Selkirk, H. B.: Dehydration of the upper troposphere and lower stratosphere by subvisible cirrus clouds near the tropical tropopause, Geophys. Res. Lett., 23, 825–828, https://doi.org/10.1029/96GL00722, 1996. a
Jensen, E. J., Lawson, P., Baker, B., Pilson, B., Mo, Q., Heymsfield, A. J., Bansemer, A., Bui, T. P., McGill, M., Hlavka, D., Heymsfield, G., Platnick, S., Arnold, G. T., and Tanelli, S.: On the importance of small ice crystals in tropical anvil cirrus, Atmos. Chem. Phys., 9, 5519–5537, https://doi.org/10.5194/acp-9-5519-2009, 2009. a, b
Jensen, E. J., Pfister, L., and Toon, O. B.: Impact of radiative heating, wind shear, temperature variability, and microphysical processes on the structure and evolution of thin cirrus in the tropical tropopause layer, J. Geophys. Res.-Atmos., 116, D12209, https://doi.org/10.1029/2010JD015417, 2011. a
Jensen, E. J., Diskin, G., Lawson, R. P., Lance, S., Bui, T. P., Hlavka, D., McGill, M., Pfister, L., Toon, O. B., and Gao, R.-S.: Ice nucleation and dehydration in the Tropical Tropopause Layer, P. Nat. Acad. Sci. USA, 110, 2041–2046, https://doi.org/10.1073/pnas.1217104110, 2013. a
Jensen, E. J., Ueyama, R., Pfister, L., Bui, T. V., Alexander, M. J., Podglajen, A., Hertzog, A., Woods, S., Lawson, R. P., Kim, J.-E., and Schoeberl, M. R.: High-frequency gravity waves and homogeneous ice nucleation in tropical tropopause layer cirrus, Geophys. Res. Lett., 43, 6629–6635, https://doi.org/10.1002/2016GL069426, 2016. a
Jensen, E. J., Pfister, L., Jordan, D. E., Bui, T. V., Ueyama, R., Singh, H. B., Thornberry, T. D., Rollins, A. W., Gao, R.-S., Fahey, D. W., Rosenlof, K. H., Elkins, J. W., Diskin, G. S., DiGangi, J. P., Lawson, R. P., Woods, S., Atlas, E. L., Rodriguez, M. A. N., Wofsy, S. C., Pittman, J., Bardeen, C. G., Toon, O. B., Kindel, B. C., Newman, P. A., McGill, M. J., Hlavka, D. L., Lait, L. R., Schoeberl, M. R., Bergman, J. W., Selkirk, H. B., Alexander, M. J., Kim, J.-E., Lim, B. H., Stutz, J., and Pfeilsticker, K.: The NASA Airborne Tropical Tropopause Experiment: High-Altitude Aircraft Measurements in the Tropical Western Pacific, B. Am. Meteorol. Soc., 98, 129–143, https://doi.org/10.1175/BAMS-D-14-00263.1, 2017. a
Jensen, E. J., Kärcher, B., Ueyama, R., Pfister, L., Bui, T. V., Diskin, G. S., DiGangi, J. P., Woods, S., Lawson, R. P., Froyd, K. D., and Murphy, D. M.: Heterogeneous ice nucleation in the tropical tropopause layer, J. Geophys. Res., 123, 12210–12227, https://doi.org/10.1029/2018JD028949, 2018a. a, b, c
Jensen, E. J., van den Heever, S. C., and Grant, L. D.: The lifecycles of ice crystals detrained from the tops of deep convection, J. Geophys. Res.-Atmos., 123, 9624– 9634, https://doi.org/10.1029/2018JD028832, 2018b. a, b, c
Jensen, E. J., Diskin, G., DiGangi, J., Woods, S., Lawson, R. P., and Bui, T. V.: Homogeneous freezing events sampled in the Tropical Tropopause Layer, J. Geophys. Res., 127, e2022JD036535, https://doi.org/10.1029/2022JD036535, 2022. a
Jiang, J. H., Yue, Q., Su, H., Kangaslahti, P., Lebsock, M., Reising, S., Schoeberl, M., Wu, L., and Herman, R. L.: Simulation of Remote Sensing of Clouds and Humidity From Space Using a Combined Platform of Radar and Multifrequency Microwave Radiometers, Earth Space Sci., 6, 1234–1243, https://doi.org/10.1029/2019EA000580, 2019. a
Jones, W. K., Christensen, M. W., and Stier, P.: A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations, Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, 2023. a, b
Judt, F., Klocke, D., Rios-Berrios, R., Vanniere, B., Ziemen, F., Auger, L., Biercamp, J., Bretherton, C., Chen, X., Düben, P., Hohenegger, C., Khairoutdinov, M., Kodama, C., Kornblueh, L., Lin, S.-J., Nakano, M., Neumann, P., Putman, W., Röber, N., Roberts, M., Satoh, M., Shibuya, R., Stevens, B., Vidale, P. L., Wedi, N., and Zhou, L.: Tropical Cyclones in Global Storm-Resolving Models, J. Meteorol. Soc. JPN II, 99, 579–602, https://doi.org/10.2151/jmsj.2021-029, 2021. a
Kalnajs, L. E., Davis, S. M., Goetz, J. D., Deshler, T., Khaykin, S., St. Clair, A., Hertzog, A., Bordereau, J., and Lykov, A.: A reel-down instrument system for profile measurements of water vapor, temperature, clouds, and aerosol beneath constant-altitude scientific balloons, Atmos. Meas. Tech., 14, 2635–2648, https://doi.org/10.5194/amt-14-2635-2021, 2021. a
Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo, D. J., and Krämer, M.: Chapter 1: Overview of Ice Nucleating Particles, Meteor. Mon., 58, 1–1, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0006.1, 2017. a, b
Kanji, Z. A., Sullivan, R. C., Niemand, M., DeMott, P. J., Prenni, A. J., Chou, C., Saathoff, H., and Möhler, O.: Heterogeneous ice nucleation properties of natural desert dust particles coated with a surrogate of secondary organic aerosol, Atmos. Chem. Phys., 19, 5091–5110, https://doi.org/10.5194/acp-19-5091-2019, 2019. a
Kärcher, B.: A parameterization of cirrus cloud formation: Revisiting competing ice nucleation, J. Geophys. Res., 127, e2022JD036907, https://doi.org/10.1029/2022JD036907, 2022. a
Kärcher, B. and Burkhardt, U.: A cirrus cloud scheme for general circulation models, Q. J. Roy. Meteorol. Soc., 134, 1439–1461, https://doi.org/10.1002/qj.301, 2008. a
Kärcher, B. and Lohmann, U.: A parameterization of cirrus cloud formation: Homogeneous freezing of supercooled aerosols, J. Geophys. Res.-Atmos., 107, AAC4-1–AAC4-10, https://doi.org/10.1029/2001JD000470, 2002. a
Kärcher, B. and Lohmann, U.: A parameterization of cirrus cloud formation: Heterogeneous freezing, J. Geophys. Res.-Atmos., 108, 4402, https://doi.org/10.1029/2002JD003220, 2003. a
Kärcher, B. and Podglajen, A.: A stochastic representation of temperature fluctuations induced by mesoscale gravity waves, J. Geophys. Res., 124, 11506–11529, https://doi.org/10.10292019JD030680, 2019. a
Kärcher, B., Hendricks, J., and Lohmann, U.: Physically based parameterization of cirrus cloud formation for use in global atmospheric models, J. Geophys. Res., 111, D01205, https://doi.org/10.1029/2005JD006219, 2006. a
Kärcher, B., DeMott, P. J., Jensen, E. J., and Harrington, J. Y.: Studies on the competition between homogeneous and heterogeneous ice nucleation in cirrus formation, J. Geophys. Res., 127, e2021JD035805, https://doi.org/10.1029/2021JD035805, 2022. a
Kato, S., Rose, F. G., Sun-Mack, S., Miller, W. F., Chen, Y., Rutan, D. A., Stephens, G. L., Loeb, N. G., Minnis, P., Wielicki, B. A., Winker, D. M., Charlock, T. P., Stackhouse, P. W., Xu, K. M., and Collins, W. D.: Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties, J. Geophys. Res.-Atmos., 116, 1–21, https://doi.org/10.1029/2011JD016050, 2011. a
Khaykin, S. M., Pommereau, J.-P., Riviere, E. D., Held, G., Ploeger, F., Ghysels, M., Amarouche, N., Vernier, J.-P., Wienhold, F. G., and Ionov, D.: Evidence of horizontal and vertical transport of water in the Southern Hemisphere tropical tropopause layer (TTL) from high-resolution balloon observations, Atmos. Chem. Phys., 16, 12273–12286, https://doi.org/10.5194/acp-16-12273-2016, 2016. a
Kim, J.-E., Alexander, M. J., Bui, T. P., Dean-Day, J. M., Lawson, R. P., Woods, S., Hlavka, D., Pfister, L., and Jensen, E. J.: Ubiquitous influence of waves on tropical high cirrus cloud, Geophys. Res. Lett., 43, 5895–5901, https://doi.org/10.1002/2016GL069293, 2016. a, b, c
Kiselev, A., Bachmann, F., Pedevilla, P., Cox, S. J., Michaelides, A., Gerthsen, D., and Leisner, T.: Active sites in heterogeneous ice nucleation—the example of K-rich feldspars, Science, 355, 367–371, https://doi.org/10.1126/science.aai8034, 2017. a
Klein, S. A., Hall, A., Norris, J. R., and Pincus, R.: Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review, Surv. Geophys., 38, 1307–1329, https://doi.org/10.1007/s10712-017-9433-3, 2017. a
Knutson, T. R. and Manabe, S.: Time-Mean Response over the Tropical Pacific to Increased C02 in a Coupled Ocean-Atmosphere Model, J. Climate, 8, 2181–2199, https://doi.org/10.1175/1520-0442(1995)008<2181:TMROTT>2.0.CO;2, 1995. a
Koll, D. D. B. and Cronin, T. W.: Earth’s outgoing longwave radiation linear due to H2O greenhouse effect, P. Nat. Acad. Sci. USA, 115, 10293–10298, https://doi.org/10.1073/pnas.1809868115, 2018. a
Koop, T., Luo, B., Tsias, A., and Peter, T.: Water activity as the determinant for homogeneous ice nucleation in aqueous solutions, Nature, 406, 611–614, 2000. a
Korolev, A. V. and Mazin, I. P.: Supersaturation of Water Vapor in Clouds, J. Atmos. Sci., 60, 2957–2974, https://doi.org/10.1175/1520-0469(2003)060<2957:SOWVIC>2.0.CO;2, 2003. a
Kox, S., Bugliaro, L., and Ostler, A.: Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing, Atmos. Meas. Tech., 7, 3233–3246, https://doi.org/10.5194/amt-7-3233-2014, 2014. a
Krämer, M., Rolf, C., Luebke, A., Afchine, A., Spelten, N., Costa, A., Meyer, J., Zöger, M., Smith, J., Herman, R. L., Buchholz, B., Ebert, V., Baumgardner, D., Borrmann, S., Klingebiel, M., and Avallone, L.: A microphysics guide to cirrus clouds – Part 1: Cirrus types, Atmos. Chem. Phys., 16, 3463–3483, https://doi.org/10.5194/acp-16-3463-2016, 2016. a, b
Krämer, M., Rolf, C., Spelten, N., Afchine, A., Fahey, D., Jensen, E., Khaykin, S., Kuhn, T., Lawson, P., Lykov, A., Pan, L. L., Riese, M., Rollins, A., Stroh, F., Thornberry, T., Wolf, V., Woods, S., Spichtinger, P., Quaas, J., and Sourdeval, O.: A microphysics guide to cirrus – Part 2: Climatologies of clouds and humidity from observations, Atmos. Chem. Phys., 20, 12569–12608, https://doi.org/10.5194/acp-20-12569-2020, 2020. a, b, c
Kretzschmar, J., Stapf, J., Klocke, D., Wendisch, M., and Quaas, J.: Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic, Atmos. Chem. Phys., 20, 13145–13165, https://doi.org/10.5194/acp-20-13145-2020, 2020. a
Krol, M., de Bruine, M., Killaars, L., Ouwersloot, H., Pozzer, A., Yin, Y., Chevallier, F., Bousquet, P., Patra, P., Belikov, D., Maksyutov, S., Dhomse, S., Feng, W., and Chipperfield, M. P.: Age of air as a diagnostic for transport timescales in global models, Geosci. Model Dev., 11, 3109–3130, https://doi.org/10.5194/gmd-11-3109-2018, 2018. a
Kuang, Z. and Bretherton, C. S.: Convective Influence on the Heat Balance of the Tropical Tropopause Layer: A Cloud-Resolving Model Study, J. Atmos. Sci., 61, 2919–2927, https://doi.org/10.1175/jas-3306.1, 2004. a
Kuang, Z. and Hartmann, D. L.: Testing the Fixed Anvil Temperature Hypothesis in a Cloud-Resolving Model, J. Climate, 20, https://doi.org/10.1175/JCLI4124.1, 2051–2057, https://doi.org/10.1175/JCLI4124.1, 2007. a, b
Kubar, T. L. and Jiang, J. H.: Net Cloud Thinning, Low-Level Cloud Diminishment, and Hadley Circulation Weakening of Precipitating Clouds with Tropical West Pacific SST Using MISR and Other Satellite and Reanalysis Data, Remote Sens., 11, 1250, https://doi.org/10.3390/rs11101250, 2019. a, b
Kubar, T. L., Hartmann, D. L., and Wood, R.: Radiative and Convective Driving of Tropical High Clouds, J. Climate, 20, 5510–5526, https://doi.org/10.1175/2007JCLI1628.1, 2007. a
Kurihana, T., Moyer, E. J., and Foster, I. T.: AICCA: AI-Driven Cloud Classification Atlas, Remote Sens., 14, 5690, https://doi.org/10.3390/rs14225690, 2022. a
Köhler, L., Green, B., and Stephan, C. C.: Comparing Loon Superpressure Balloon Observations of Gravity Waves in the Tropics With Global Storm-Resolving Models, J. Geophys. Res.-Atmos., 128, e2023JD038549, https://doi.org/10.1029/2023JD038549, 2023. a
Ladino, L. A., Korolev, A., Heckman, I., Wolde, M., Fridlind, A. M., and Ackerman, A. S.: On the role of ice-nucleating aerosol in the formation of ice particles in tropical mesoscale convective systems, Geophys. Res. Lett., 44, 1574–1582, https://doi.org/10.1002/2016GL072455, 2017. a
Ladstädter, Steiner, F. A. K., and Gleisner, H.: Resolving the 21st century temperature trends of the upper troposphere-lower stratosphere with satellite observations, Nature, 13, 2023, https://doi.org/10.1038/s41598-023-28222-x, 2023. a
Lamb, D. and Verlinde, J.: Physics and Chemistry of Clouds, University Press, Cambridge, NY, https://doi.org/10.1017/CBO9780511976377, 2011. a
Lamb, K. D., Clouser, B. W., Bolot, M., Sarkozy, L., Ebert, V., Saathoff, H., Möhler, O., and Moyer, E. J.: Laboratory measurements of HDO/H2O isotopic fractionation during ice deposition in simulated cirrus clouds, P. Natl. Acad. Sci. USA, 114, 5612–5617, https://doi.org/10.1073/pnas.1618374114, 2017. a, b
Lamb, K. D., Harrington, J. Y., Clouser, B. W., Moyer, E. J., Sarkozy, L., Ebert, V., Möhler, O., and Saathoff, H.: Re-evaluating cloud chamber constraints on depositional ice growth in cirrus clouds – Part 1: Model description and sensitivity tests, Atmos. Chem. Phys., 23, 6043–6064, https://doi.org/10.5194/acp-23-6043-2023, 2023. a
Lamquin, N., Stubenrauch, C. J., Gierens, K., Burkhardt, U., and Smit, H.: A global climatology of upper-tropospheric ice supersaturation occurrence inferred from the Atmospheric Infrared Sounder calibrated by MOZAIC, Atmos. Chem. Phys., 12, 381–405, https://doi.org/10.5194/acp-12-381-2012, 2012. a
Lamraoui, F., Krämer, M., Afchine, A., Sokol, A. B., Khaykin, S., Pandey, A., and Kuang, Z.: Sensitivity of convectively driven tropical tropopause cirrus properties to ice habits in high-resolution simulations, Atmos. Chem. Phys., 23, 2393–2419, https://doi.org/10.5194/acp-23-2393-2023, 2023. a, b
Lane, T. P., Sharman, R. D., Clark, T. L., and Hsu, H.-M.: An Investigation of Turbulence Generation Mechanisms above Deep Convection, J. Atmos. Sci., 60, 1297–1321, https://doi.org/10.1175/1520-0469(2003)60<1297:AIOTGM>2.0.CO;2, 2003. a
L'Ecuyer, T. S., Wood, N. B., Haladay, T., Stephens, G. L., and Stackhouse, P. W.: Impact of clouds on atmospheric heating based on the R04 CloudSat fluxes and heating rates data set, J. Geophys. Res., 113, D00A15, https://doi.org/10.1029/2008JD009951, 2008. a
Lee, J., Yang, P., Dessler, A. E., Gao, B.-C., and Platnick, S.: Distribution and Radiative Forcing of Tropical Thin Cirrus Clouds, J. Atmos. Sci., 66, 3721–3731, https://doi.org/10.1175/2009JAS3183.1, 2009. a
Leonarski, L., C.-Labonnote, L., Compiègne, M., Vidot, J., Baran, A. J., and Dubuisson, P.: Potential of Hyperspectral Thermal Infrared Spaceborne Measurements To Retrieve Ice Cloud Physical Properties: Case Study of Iasi and Iasi-Ng, Remote Sens., 13, 116, https://doi.org/10.3390/rs13010116, 2020. a
Li, R. L., Storelvmo, T., Fedorov, A. V., and Choi, Y.-S.: A Positive Iris Feedback: Insights from Climate Simulations with Temperature-Sensitive Cloud–Rain Conversion, J. Climate, 32, 5305–5324, https://doi.org/10.1175/JCLI-D-18-0845.1, 2019. a, b
Li, Y. and Thompson, D. W. J.: The signature of the stratospheric Brewer‒Dobson circulation in tropospheric clouds, J. Geophys. Res.-Atmos., 118, 3486–3494, https://doi.org/10.1002/jgrd.50339, 2013. a
Lian, S., Zhou, L., Murphy, D. M., Froyd, K. D., Toon, O. B., and Yu, P.: Global distribution of Asian, Middle Eastern, and North African dust simulated by CESM1/CARMA, Atmos. Chem. Phys., 22, 13659–13676, https://doi.org/10.5194/acp-22-13659-2022, 2022. a
Libbrecht, K. G.: The physics of snow crystals, Rep. Prog. Phys., 68, 855, https://doi.org/10.1088/0034-4885/68/4/R03, 2005. a, b
Lilly, D. K.: Cirrus outflow dynamics, J. Atmos. Sci., 45, 1594–1605, https://doi.org/10.1175/1520-0469(1988)045<1594:COD>2.0.CO;2, 1988. a, b
Lin, B., Wielicki, B. A., Chambers, L. H., Hu, Y., and Xu, K.-M.: The Iris Hypothesis: A Negative or Positive Cloud Feedback?, J. Climate, 15, 3–7, https://doi.org/10.1175/1520-0442(2002)015<0003:TIHANO>2.0.CO;2, 2002. a
Lindzen, R. S., Chou, M. D., and Hou, A. Y.: Does the Earth Have an Adaptive Infrared Iris?, B. Am. Meteorol. Soc., 82, 417–432, https://doi.org/10.1175/1520-0477(2001)082<0417:DTEHAA>2.3.CO;2, 2001. a, b, c, d
Liu, C., Zipser, E. J., and Nesbitt, S. W.: Global Distribution of Tropical Deep Convection: Different Perspectives from TRMM Infrared and Radar Data, J. Climate, 20, 489–503, https://doi.org/10.1175/JCLI4023.1, 2007. a
Liu, R., Liou, K.-N., Su, H., Gu, Y., Zhao, B., Jiang, J. H., and Liu, S. C.: High cloud variations with surface temperature from 2002 to 2015: Contributions to atmospheric radiative cooling rate and precipitation changes, J. Geophys. Res.-Atmos., 122, 5457–5471, https://doi.org/10.1002/2016JD026303, 2017. a, b
Luke, E. P., Yang, F., Kollias, P., Vogelmann, A. M., and Maahn, M.: New insights into ice multiplication using remote-sensing observations of slightly supercooled mixed-phase clouds in the Arctic, P. Natl. Acad. Sci. USA, 118, e2021387118, https://doi.org/10.1073/pnas.2021387118, 2021. a
Luo, Z. and Rossow, W. B.: Characterizing Tropical Cirrus Life Cycle, Evolution, and Interaction with Upper-Tropospheric Water Vapor Using Lagrangian Trajectory Analysis of Satellite Observations, J. Climate, 17, 4541–4563, https://doi.org/10.1175/3222.1, 2004. a, b, c, d
L’Ecuyer, T. S., Hang, Y., Matus, A. V., and Wang, Z.: Reassessing the Effect of Cloud Type on Earth’s Energy Balance in the Age of Active Spaceborne Observations. Part I: Top of Atmosphere and Surface, J. Climate, 32, 6197–6217, https://doi.org/10.1175/JCLI-D-18-0753.1, 2019. a
Mace, G. G., Deng, M., Soden, B., and Zipser, E.: Association of Tropical Cirrus in the 10–15-km Layer with Deep Convective Sources: An Observational Study Combining Millimeter Radar Data and Satellite-Derived Trajectories, J. Atmos. Sci., 63, 480–503, https://doi.org/10.1175/JAS3627.1, 2006. a
Magee, N. B., Miller, A., Amaral, M., and Cumiskey, A.: Mesoscopic surface roughness of ice crystals pervasive across a wide range of ice crystal conditions, Atmos. Chem. Phys., 14, 12357–12371, https://doi.org/10.5194/acp-14-12357-2014, 2014. a
Maher, P., Gerber, E. P., Medeiros, B., Merlis, T. M., Sherwood, S., Sheshadri, A., Sobel, A. H., Vallis, G. K., Voigt, A., and Zurita-Gotor, P.: Model Hierarchies for Understanding Atmospheric Circulation, Rev. Geophys., 57, 250–280, https://doi.org/10.1029/2018RG000607, 2019. a
Maloney, C., Bardeen, C., Toon, O. B., Jensen, E., Woods, S., Thornberry, T., Pfister, L., Diskin, G., and Bui, T. P.: An evaluation of the representation of tropical tropopause cirrus in the CESM/CARMA model using satellite and aircraft observations, J. Geophys. Res., 124, 8659–8687, https://doi.org/10.1029/2018JD029720, 2019. a
Marcolli, C.: Deposition nucleation viewed as homogeneous or immersion freezing in pores and cavities, Atmos. Chem. Phys., 14, 2071–2104, https://doi.org/10.5194/acp-14-2071-2014, 2014. a
Marcolli, C., Mahrt, F., and Kärcher, B.: Soot PCF: pore condensation and freezing framework for soot aggregates, Atmos. Chem. Phys., 21, 7791–7843, https://doi.org/10.5194/acp-21-7791-2021, 2021. a
Massie, S. T., Gille, J., Craig, C., Khosravi, R., Barnett, J., Read, W., and Winker, D.: HIRDLS and CALIPSO observations of tropical cirrus, J. Geophys. Res.-Atmos., 115, D00H11, https://doi.org/10.1029/2009JD012100, 2010. a
Matus, A. V. and L'Ecuyer, T. S.: The role of cloud phase in Earth's radiation budget, J. Geophys. Res.-Atmos., 122, 2559– 2578, https://doi.org/10.1002/2016JD025951, 2017. a, b
Mauritsen, T. and Stevens, B.: Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models, Nat. Geosci., 8, 346–351, https://doi.org/10.1038/ngeo2414, 2015. a, b
May, P. T., Mather, J. H., Vaughan, G., Jakob, C., McFarquhar, G. M., Bower, K. N., and Mace, G. G.: The Tropical Warm Pool International Cloud Experiment, B. Am. Meteorol.l Soc., 89, 629–646, https://doi.org/10.1175/BAMS-89-5-629, 2008. a
McKim, B., Bony, S., and Dufresne, J.-L.: Physical and observational constraints on the anvil cloud feedback, ESS Open Archive [preprint], https://doi.org/10.22541/au.167769953.39966398/v2, 11 June 2023. a
Menzel, W. P.: Cloud Tracking with Satellite Imagery: From the Pioneering Work of Ted Fujita to the Present, B. Am. Meteorol. Soc., 82, 33–48, https://doi.org/10.1175/1520-0477(2001)082<0033:CTWSIF>2.3.CO;2, 2001. a
Mitchell, D. L., Garnier, A., Pelon, J., and Erfani, E.: CALIPSO (IIR–CALIOP) retrievals of cirrus cloud ice-particle concentrations, Atmos. Chem. Phys., 18, 17325–17354, https://doi.org/10.5194/acp-18-17325-2018, 2018. a
Morrison, H. and Milbrandt, J. A.: Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests, J. Atmos. Sci., 72, 287–311, https://doi.org/10.1175/JAS-D-14-0065.1, 2015. a
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W., Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A., Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S. I., van Diedenhoven, B., and Xue, L.: Confronting the Challenge of Modeling Cloud and Precipitation Microphysics, J. Adv. Model. Earth Systems, 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020. a, b, c
Muench, S. and Lohmann, U.: Developing a cloud scheme with prognostic cloud fraction and two moment microphysics for ECHAM-HAM, J. Adv. Model. Earth Sys., 12, e2019MS001824, https://doi.org/10.1029/2019MS001824, 2020. a
Mülmenstädt, J., Nam, C., Salzmann, M., Kretzschmar, J., L’Ecuyer, T. S., Lohmann, U., Ma, P.-L., Myhre, G., Neubauer, D., Stier, P., Suzuki, K., Wang, M., and Quaas, J.: Reducing the aerosol forcing uncertainty using observational constraints on warm rain processes, Sci. Adv., 6, eaaz6433, https://doi.org/10.1126/sciadv.aaz6433, 2020. a
Murray, B. J., Carslaw, K. S., and Field, P. R.: Opinion: Cloud-phase climate feedback and the importance of ice-nucleating particles, Atmos. Chem. Phys., 21, 665–679, https://doi.org/10.5194/acp-21-665-2021, 2021. a
NASA/LARC/SD/ASDC: CALIPSO Lidar Level 3 Global Energy and Water Cycle Experiment (GEWEX) Cloud, Standard V1-00, https://doi/org/10.5067/CALIOP/CALIPSO/LID_L3_GEWEX, 2019. a
Nelson, J.: Theory of isotopic fractionation on facetted ice crystals, Atmos. Chem. Phys., 11, 11351–11360, https://doi.org/10.5194/acp-11-11351-2011, 2011. a
Norris, J. R., Allen, R. J., Evan, A. T., Zelinka, M. D., O'Dell, C. W., and Klein, S. A.: Evidence for climate change in the satellite cloud record, Nature, 536, 72–75, https://doi.org/10.1038/NATURE18273, 2016. a
Parol, F., Buriez, J. C., Brogniez, G., and Fouquart, Y.: Information content of AVHRR channel 4 and 5 with respect to the effective radius of cirrus cloud particles, J. Appl. Meteor., 30, 973–984, 1991. a
Phillips, V. T. J., DeMott, P. J., and Andronache, C.: An Empirical Parameterization of Heterogeneous Ice Nucleation for Multiple Chemical Species of Aerosol, J. Atmos. Sci., 65, 2757–2783, https://doi.org/10.1175/2007JAS2546.1, 2008. a, b
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Patrick Hofmann, R. J.: Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators, J. Climate, 25, 4699–4720, https://doi.org/10.1175/JCLI-D-11-00267.1, 2012. a
Podglajen, A., Hertzog, A., Plougonven, R., and Legras, B.: Lagrangian temperature and vertical velocity fluctuations due to gravity waves in the lower stratosphere, Geophys. Res. Lett., 43, 3543–3553, https://doi.org/10.1002/2016GL068148, 2016. a, b
Podglajen, A., Bui, T. P., Dean-Day, J. M., Pfister, L., Jensen, E. J., Alexander, M. J., Hertzog, A., Kärcher, B., Plougonven, R., and Randel, W. J.: Small-scale wind fluctuations in the tropical tropopause layer from aircraft measurements: Occurrence, nature and impact on vertical mixing, J. Atmos. Sci., 74, 3847–3869, https://doi.org/10.1175/JAS-D-17-0010.1, 2017. a
Podglajen, A., Plougonven, R., Hertzog, A., and Jensen, E.: Impact of gravity waves on the motion and distribution of atmospheric ice particles, Atmos. Chem. Phys., 18, 10799–10823, https://doi.org/10.5194/acp-18-10799-2018, 2018. a
Podglajen, A., Hertzog, A., Plougonven, R., and Legras, B.: Lagrangian gravity wave spectra in the lower stratosphere of current (re)analyses, Atmos. Chem. Phys., 20, 9331–9350, https://doi.org/10.5194/acp-20-9331-2020, 2020. a
Pokrifka, G. F., Moyle, A. M., Hanson, L. E., and Harrington, J. Y.: Estimating surface attachment kinetic and growth transition influences on vapor-grown ice crystals, J. Atmos. Sci., 70, 2393–2410, https://doi.org/10.1175/JAS-D-19-0303.1, 2020. a
Popp, M. and Silvers, L. G.: Double and Single ITCZs with and without Clouds, J. Climate, 30, 9147–9166, https://doi.org/10.1175/JCLI-D-17-0062.1, 2017. a
Porterfield, M.: Data from NASA's Missions, Research, and Activities, https://www.nasa.gov/open/data.html (last access: 31 March 2023), 2021. a
Powell, S. W., Houze, R. A. J., Kumar, A., and McFarlane, S. A.: Comparison of Simulated and Observed Continental Tropical Anvil Clouds and Their Radiative Heating Profiles, J. Atmos. Sci., 69, 2662–2681, https://doi.org/10.1175/jas-d-11-0251.1, 2012. a
Prabhakaran, P., Kinney, G., Cantrell, W., Shaw, R. A., and Bodenschatz, E.: High supersaturation in the wake of falling hydrometeors: Implications for cloud invigoration and ice nucleation, Geophys. Res. Lett., 47, e2020GL088055, https://doi.org/10.1029/2020GL088055, 2020. a
Prein, A. F., Rasmussen, R. M., Wang, D., and Giangrande, S. E.: Sensitivity of organized convective storms to model grid spacing in current and future climates, Philos. T. R. Soc. A, 379, 20190546, https://doi.org/10.1098/rsta.2019.0546, 2021. a
Proske, U., Ferrachat, S., Neubauer, D., Staab, M., and Lohmann, U.: Assessing the potential for simplification in global climate model cloud microphysics, Atmos. Chem. Phys., 22, 4737–4762, https://doi.org/10.5194/acp-22-4737-2022, 2022. a, b
Protopapadaki, S. E., Stubenrauch, C. J., and Feofilov, A. G.: Upper tropospheric cloud systems derived from IR sounders: properties of cirrus anvils in the tropics, Atmos. Chem. Phys., 17, 3845–3859, https://doi.org/10.5194/acp-17-3845-2017, 2017. a, b
Qu, Z., Korolev, A., Milbrandt, J. A., Heckman, I., Huang, Y., McFarquhar, G. M., Morrison, H., Wolde, M., and Nguyen, C.: The impacts of secondary ice production on microphysics and dynamics in tropical convection, Atmos. Chem. Phys., 22, 12287–12310, https://doi.org/10.5194/acp-22-12287-2022, 2022. a
Ramanathan, V. and Collins, W.: Thermodynamic regulation of ocean warming by cirrus clouds deduced from observations of the 1987 El Niño, Nature, 351, 27–32, https://doi.org/10.1038/351027a0, 1991. a
Ramanathan, V., Cess, R. D., Harrison, E. F., Minnis, P., Barkstrom, B. R., Ahmad, E., and Hartmann, D.: Cloud-radiative forcing and climate: Results from the earth radiation budget experiment, Science, 243, 57–63, https://doi.org/10.1126/science.243.4887.57, 1989. a
Randel, W. J. and Jensen, E. J.: Physical processes in the tropical tropopause layer and their roles in a changing climate, Nat. Geosci., 6, 169–176, https://doi.org/10.1038/ngeo1733, 2013. a
Ravetta, F., Mariage, V., Brousse, E., DÁlmeida, E., Ferreira, F., Pelon, J., and Victori, S.: BeCOOL: A Balloon-Borne Microlidar System Designed for Cirrus and Convective Overshoot Monitoring, EPJ Web Conf., 237, 7003, https://doi.org/10.1051/epjconf/202023707003, 2020. a
Raymond, D. J.: A New Model of the Madden–Julian Oscillation, J. Atmos. Sci., 58, 2807–2819, https://doi.org/10.1175/1520-0469(2001)058<2807:ANMOTM>2.0.CO;2, 2001. a
Reutter, P., Neis, P., Rohs, S., and Sauvage, B.: Ice supersaturated regions: properties and validation of ERA-Interim reanalysis with IAGOS in situ water vapour measurements, Atmos. Chem. Phys., 20, 787–804, https://doi.org/10.5194/acp-20-787-2020, 2020. a
Riley, E. M., Mapes, B. E., and Tulich, S. N.: Clouds Associated with the Madden–Julian Oscillation: A New Perspective from CloudSat, J. Atmos. Sci., 68, 3032–3051, https://doi.org/10.1175/JAS-D-11-030.1, 2011. a
Robert A Houze, J.: Cloud Clusters and Large-Scale Vertical Motions in the Tropics, J. Meteorol. Soc. Jap, 60, 396–410, https://doi.org/10.2151/jmsj1965.60.1_396, 1982. a
Roca, R., Fiolleau, T., and Bouniol, D.: A simple model of the life cycle of mesoscale convective systems cloud shield in the tropics, J. Climate, 30, 4283–4298, https://doi.org/10.1175/JCLI-D-16-0556.1, 2017. a, b
Roh, W., Satoh, M., and Hohenegger, C.: Intercomparison of Cloud Properties in DYAMOND Simulations over the Atlantic Ocean, J. Meteorol. Soc. Jpn, 99, 1439–1451, https://doi.org/10.2151/jmsj.2021-070, 2021. a
Rollins, A. W., Thornberry, T. D., Gao, R. S., Woods, S., Lawson, R. P., Bui, T. P., Jensen, E. J., and Fahey, D. W.: Observational constraints on the efficiency of dehydration mechanisms in the tropical tropopause layer, Geophys. Res. Lett., 43, 2912–2918, https://doi.org/10.1002/2016GL067972, 2016. a
Rosenfeld, D. and Woodley, W. L.: Deep convective clouds with sustained supercooled liquid water down to −37.5 ∘C, Nature, 405, 440–442, https://doi.org/10.1038/35013030, 2000. a
Rossow, W. B. and Schiffer, R. A.: Advances in Understanding Clouds from ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2, 1999. a, b
Saint-Lu, M., Bony, S., and Dufresne, J.-L.: Observational Evidence for a Stability Iris Effect in the Tropics, Geophys. Res. Lett., 47, e2020GL089059, https://doi.org/10.1029/2020GL089059, 2020. a, b
Saint-Lu, M., Bony, S., and Dufresne, J.-L.: Clear-sky control of anvils in response to increased CO2 or surface warming or volcanic eruptions, npj Clim. Atmos. Sci., 5, 1–8, https://doi.org/10.1038/s41612-022-00304-z, number: 1 Publisher: Nature Publishing Group, 2022. a, b
Saleeby, S. M. and van den Heever, S. C.: Developments in the CSU-RAMS Aerosol Model: Emissions, Nucleation, Regeneration, Deposition, and Radiation, J. Appl. Meteorol. Climatol., 52, 2601–2622, https://doi.org/10.1175/JAMC-D-12-0312.1, 2013. a
Sassen, K., Wang, Z., and Liu, D.: Cirrus clouds and deep convection in the tropics: Insights from CALIPSO and CloudSat, J. Geophys. Res.-Atmos., 114, 1–11, https://doi.org/10.1029/2009JD011916, 2009. a
Sauter, K., L'Ecuyer, T. S., van den Heever, S. C., Twohy, C., Heidinger, A., Wanzong, S., and Wood, N.: The Observed Influence of Tropical Convection on the Saharan Dust Layer, J. Geophys. Res.-Atmos., 124, 10896–10912, https://doi.org/10.1029/2019JD031365, 2019. a
Scherllin-Pirscher, B. ., Steiner, A. K., Anthes, R. A., Alexander, M. J., Alexander, S. P., Biondi, R., Birner, T., Kim, J., Randel, W. J., Son, S.-W., Tsuda, T., and Zeng, Z.: Tropical temperature variability in the UTLS: New insights from GPS radio occultation observations, J. Climate, 34, 2813–2838, https://doi.org/10.1175/JCLI-D-20-0385.1, 2021. a
Schmidt, C. T. and Garrett, T. J.: A Simple Framework for the Dynamic Response of Cirrus Clouds to Local Diabatic Radiative Heating, J. Atmos. Sci., 70, 1409–1422, https://doi.org/10.1175/JAS-D-12-056.1, 2013. a, b
Schneider, J., Höhler, K., Wagner, R., Saathoff, H., Schnaiter, M., Schorr, T., Steinke, I., Benz, S., Baumgartner, M., Rolf, C., Krämer, M., Leisner, T., and Möhler, O.: High homogeneous freezing onsets of sulfuric acid aerosol at cirrus temperatures, Atmos. Chem. Phys., 21, 14403–14425, https://doi.org/10.5194/acp-21-14403-2021, 2021. a, b
Schumacher, C., Houze, R. A. J., and Kraucunas, I.: The Tropical Dynamical Response to Latent Heating Estimates Derived from the TRMM Precipitation Radar, J. Atmos. Sci., 61, 1341–1358, https://doi.org/10.1175/1520-0469(2004)061<1341:TTDRTL>2.0.CO;2, 2004. a
Seeley, J. T., Jeevanjee, N., Langhans, W., and Romps, D. M.: Formation of Tropical Anvil Clouds by Slow Evaporation, Geophys. Res. Lett., 46, 492–501, https://doi.org/10.1029/2018GL080747, 2019. a, b
Seidel, S. D. and Yang, D.: Temperatures of Anvil Clouds and Radiative Tropopause in a Wide Array of Cloud-Resolving Simulations, J. Climate, 35, 8065–8078, https://doi.org/10.1175/JCLI-D-21-0962.1, 2022. a
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., Heydt, A. S. v. d., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a, b, c
Shima, S., Sato, Y., Hashimoto, A., and Misumi, R.: Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5–2.2.0, −2.2.1, and −2.2.2, Geosci. Model Dev., 13, 4107–4157, https://doi.org/10.5194/gmd-13-4107-2020, 2020. a
Shindell, D. T.: Climate and ozone response to increased stratospheric water vapor, Geophys. Res. Lett., 28, 1551–1554, https://doi.org/10.1029/1999GL011197, 2001. a
Skamarock, W. C., Snyder, C., Klemp, J. B., and Park, S.-H.: Vertical Resolution Requirements in Atmospheric Simulation, Mon. Weather Rev., 147, 2641–2656, https://doi.org/10.1175/MWR-D-19-0043.1, 2019. a
Skrotzki, J., Connolly, P., Schnaiter, M., Saathoff, H., Möhler, O., Wagner, R., Niemand, M., Ebert, V., and Leisner, T.: The accommodation coefficient of water molecules on ice – cirrus cloud studies at the AIDA simulation chamber, Atmos. Chem. Phys., 13, 4451–4466, https://doi.org/10.5194/acp-13-4451-2013, 2013. a, b
Sokol, A. B.: gasparini_et_al_2023, Github [code], https://github.com/adambsokol/gasparini_et_al_2023 (last access: last access: 14 December 2023), 2023. a
Sokol, A. B. and Hartmann, D. L.: Tropical Anvil Clouds: Radiative Driving Toward a Preferred State, J. Geophys. Res.-Atmos., 125, e2020JD033107, https://doi.org/10.1029/2020JD033107, 2020. a, b, c
Sölch, I. and Kärcher, B.: A large-eddy model for cirrus cloudsvwith explicit aerosol and ice microphysics and Lagrangian ice particle tracking, Q. J. Roy. Meteorol. Soc., 136, 2074–2093, https://doi.org/10.1002/qj.689, 2010. a, b
Sölch, I. and Kärcher, B.: Process-oriented large-eddy simulations of a midlatitude cirrus cloud system based on observations, Q. J. Roy. Meteorol. Soc., 137, 374–393, https://doi.org/10.1002/qj.764, 2011. a
Solomon, S., Rosenlof, K., Portmann, R., Daniel, J., Davis, S., Sanford, T., and Plattner, G.-K.: Contributions of stratospheric water vapor changes to decadal variations in the rate of global warming, Science, 327, 1219–1223, https://doi.org/10.1126/science.1182488, 2010. a, b
Sourdeval, O., Gryspeerdt, E., Krämer, M., Goren, T., Delanoë, J., Afchine, A., Hemmer, F., and Quaas, J.: Ice crystal number concentration estimates from lidar–radar satellite remote sensing – Part 1: Method and evaluation, Atmos. Chem. Phys., 18, 14327–14350, https://doi.org/10.5194/acp-18-14327-2018, 2018. a, b
Stauffer, C. L. and Wing, A. A.: Properties, Changes, and Controls of Deep-Convecting Clouds in Radiative-Convective Equilibrium, J. Adv. Model. Earth Syst., 14, e2021MS002917, https://doi.org/10.1029/2021MS002917, 2022. a, b, c
Stein, T. H. M., Holloway, C. E., Tobin, I., and Bony, S.: Observed Relationships between Cloud Vertical Structure and Convective Aggregation over Tropical Ocean, J. Climate, 30, 2187–2207, https://doi.org/10.1175/JCLI-D-16-0125.1, 2017. a, b
Stephan, C. C., Strube, C., Klocke, D., Ern, M., Hoffmann, L., Preusse, P., and Schmidt, H.: Gravity waves in global high-resolution simulations with explicit and parameterized convection, J. Geophys. Res., 124, 4446–4459, https://doi.org/10.1029/2018JD030073, 2019. a
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C., L'Ecuyer, T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten Years of Actively Observing the Earth System, B. Am. Meteorol. Soc., 99, 569–581, https://doi.org/10.1175/BAMS-D-16-0324.1, 2018. a
Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., Durden, S., Rokey, M., Reinke, D., Partain, P., Mace, G. G., Austin, R., L'Ecuyer, T. S., Haynes, J., Lebsock, M., Suzuki, K., Waliser, D., Wu, D., Kay, J., Gettelman, A., Wang, Z., and Marchand, R.: CloudSat mission: Performance and early science after the first year of operation, J. Geophys. Res.-Atmos., 114, 1–18, https://doi.org/10.1029/2008JD009982, 2008. a
Stevens, B. and Brenguier, J.-L.: Cloud-controlling Factors: Low Clouds, in: Clouds in the Perturbed Climate System: Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation, The MIT Press, ISBN 9780262012874, https://doi.org/10.7551/mitpress/9780262012874.003.0008, 2009. a
Stevens, B. and Kluft, L.: A Colorful look at Climate Sensitivity, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1460, 2023. a
Stevens, B., Acquistapace, C., Hansen, A., Heinze, R., Klinger, C., Klocke, D., Rybka, H., Schubotz, W., Windmiller, J., Adamidis, P., Arka, I., Barlakas, V., Biercamp, J., Brueck, M., Brune, S., Buehler, S. A., Burkhardt, U., Cioni, G., Costa-Surós, M., Crewell, S., Crüger, T., Deneke, H., Friederichs, P., Henken, C. C., Hohenegger, C., Jacob, M., Jakub, F., Kalthoff, N., Köhler, M., van LAAR, T. W., Li, P., Löhnert, U., Macke, A., Madenach, N., Mayer, B., Nam, C., Naumann, A. K., Peters, K., Poll, S., Quaas, J., Röber, N., Rochetin, N., Scheck, L., Schemann, V., Schnitt, S., Seifert, A., Senf, F., Shapkalijevski, M., Simmer, C., Singh, S., Sourdeval, O., Spickermann, D., Strandgren, J., Tessiot, O., Vercauteren, N., Vial, J., Voigt, A., and Zängl, G.: The added value of large-eddy and storm-resolving models for simulating clouds and precipitation, J. Meteorol. Soc. Jpn, 98, 395–435, https://doi.org/10.2151/jmsj.2020-021, 2020. a
Strandgren, J., Fricker, J., and Bugliaro, L.: Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI, Atmos. Meas. Tech., 10, 4317–4339, https://doi.org/10.5194/amt-10-4317-2017, 2017. a, b
Strapp, J., Schwarzenboeck, A., Bedka, K., Bond, T., Calmels, A., Delanoë, J., Dezitter, F., Grzych, M., Harrah, S., Korolev, A., Leroy, D., Lilie, L., Mason, J., Potts, R., Protat, A., Ratvasky, T., Riley, J., and Wolde, M.: An Assessment of Cloud Total Water Content and Particle Size from Flight Test Campaign Measurements in High Ice Water Content, Mixed Phase/Ice Crystal Icing Conditions: Primary In-Situ Measurements, Tech. rep., Federal Aviation Administration, https://rosap.ntl.bts.gov/view/dot/57746 (last access: 14 December 2023), 2020. a, b
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G., Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A., Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen, C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao, G.: Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX radiation panel, B. Am. Meteorol. Soc., 94, 1031–1049, https://doi.org/10.1175/BAMS-D-12-00117.1, 2013. a
Stubenrauch, C. J., Feofilov, A. G., Protopapadaki, S. E., and Armante, R.: Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications, Atmos. Chem. Phys., 17, 13625–13644, https://doi.org/10.5194/acp-17-13625-2017, 2017. a, b
Stubenrauch, C. J., Bonazzola, M., Protopapadaki, S. E., and Musat, I.: New Cloud System Metrics to Assess Bulk Ice Cloud Schemes in a GCM, J. Adv. Model. Earth Syst., 11, 3212–3234, https://doi.org/10.1029/2019MS001642, 2019. a
Stubenrauch, C. J., Mandorli, G., and Lemaitre, E.: Convective organization and 3D structure of tropical cloud systems deduced from synergistic A-Train observations and machine learning, Atmos. Chem. Phys., 23, 5867–5884, https://doi.org/10.5194/acp-23-5867-2023, 2023. a
Su, H., Jiang, J. H., Gu, Y., Neelin, J. D., Kahn, B. H., Feldman, D., Yung, Y. L., Waters, J. W., Livesey, N. J., Santee, M. L., and Read, W. G.: Variations of tropical upper tropospheric clouds with sea surface temperature and implications for radiative effects, J. Geophys. Res.-Atmos., 113, D10211, https://doi.org/10.1029/2007JD009624, 2008. a
Su, H., Jiang, J. H., Neelin, J. D., Shen, T. J., Zhai, C., Yue, Q., Wang, Z., Huang, L., Choi, Y.-S., Stephens, G. L., and Yung, Y. L.: Tightening of tropical ascent and high clouds key to precipitation change in a warmer climate, Nat. Commun., 8, 15771, https://doi.org/10.1038/ncomms15771, 2017. a
Sullivan, S. C. and Hoose, C.: Science of Cloud and Climate Science: An Analysis of the Literature Over the Past 50 Years, in: Clouds and Their Climatic Impact: Radiation, Circulation, and Precipitation, edited by: Sullivan, S. C. and Hoose, C., Wiley–American Geophysical Union, 1-14, ISBN 978-1-119-70031-9, 2024. a
Sullivan, S., Voigt, A., Miltenberger, A., Rolf, C., and Krämer, M.: A Lagrangian Perspective of Microphysical Impact on Ice Cloud Evolution and Radiative Heating, J. Adv. Model. Earth Syst., 14, e2022MS003226, https://doi.org/10.1029/2022MS003226, 2022. a, b, c
Sullivan, S. C. and Voigt, A.: Ice microphysical processes exert a strong control on the simulated radiative energy budget in the tropics, Commun. Earth Environ., 2, 137, https://doi.org/10.1038/s43247-021-00206-7, 2021. a, b, c, d
Sullivan, S. C., Lee, D., Oreopoulos, L., and Nenes, A.: Role of updraft velocity in temporal variability of global cloud hydrometeor number, P. Natl. Acad. Sci. USA, 113, 5791–5796, https://doi.org/10.1073/pnas.1514039113, 2016. a
Sullivan, S. C., Hoose, C., and Nenes, A.: Investigating the contribution of secondary ice production to in-cloud ice crystal numbers, J. Geophys. Res.-Atmos., 122, 9391–9412, https://doi.org/10.1002/2017JD026546, 2017. a
Sullivan, S. C., Hoose, C., Kiselev, A., Leisner, T., and Nenes, A.: Initiation of secondary ice production in clouds, Atmos. Chem. Phys., 18, 1593–1610, https://doi.org/10.5194/acp-18-1593-2018, 2018. a
Sullivan, S. C., Schiro, K. A., Stubenrauch, C., and Gentine, P.: The Response of Tropical Organized Convection to El Niño Warming, J. Geophys. Res.-Atmos., 124, 8481–8500, https://doi.org/10.1029/2019JD031026, 2019. a, b
Sweeney, A., Fu, Q., Pahlavan, H. A., and Haynes, P.: Seasonality of the QBO Impact on Equatorial Clouds, J. Geophys. Res.-Atmos., 128, e2022JD037737, https://doi.org/10.1029/2022JD037737, 2023. a, b
Thuburn, J. and Craig, G. C.: On the temperature structure of the tropical substratosphere, J. Geophys. Res., 107, 4017, https://doi.org/10.1029/2001JD000448, 2002. a
Tiedtke, M.: Representation of Clouds in Large-Scale Models, Mon. Weather Rev., 121, 3040–3061, https://doi.org/10.1175/1520-0493(1993)121<3040:ROCILS>2.0.CO;2, 1993. a
Tobin, I., Bony, S., and Roca, R.: Observational Evidence for Relationships between the Degree of Aggregation of Deep Convection, Water Vapor, Surface Fluxes, and Radiation, J. Climate, 25, 6885–6904, https://doi.org/10.1175/JCLI-D-11-00258.1, 2012. a
Tobin, I., Bony, S., Holloway, C. E., Grandpeix, J.-Y., Sèze, G., Coppin, D., Woolnough, S. J., and Roca, R.: Does convective aggregation need to be represented in cumulus parameterizations?, J. Adv. Model. Earth Syst., 5, 692–703, https://doi.org/10.1002/jame.20047, 2013. a
Tselioudis, G., Rossow, W. B., Jakob, C., Remillard, J., Tropf, D., and Zhang, Y.: Evaluation of Clouds, Radiation, and Precipitation in CMIP6 Models Using Global Weather States Derived from ISCCP-H Cloud Property Data, J. Climate, 34, 7311–7324, https://doi.org/10.1175/JCLI-D-21-0076.1, 2021. a
Tseng, H. H. and Fu, Q.: Temperature Control of the Variability of Tropical Tropopause Layer Cirrus Clouds, J. Geophys. Res.-Atmos., 122, 11,062–11,075, https://doi.org/10.1002/2017JD027093, 2017. a, b
Tsushima, Y., Iga, S.-i., Tomita, H., Satoh, M., Noda, A. T., and Webb, M. J.: High cloud increase in a perturbed SST experiment with a global nonhydrostatic model including explicit convective processes, J. Adv. Model. Earth Syst., 6, 571–585, https://doi.org/10.1002/2013MS000301, 2014. a
Turbeville, S. M., Nugent, J. M., Ackerman, T. P., Bretherton, C. S., and Blossey, P. N.: Tropical Cirrus in Global Storm-Resolving Models: 2. Cirrus Life Cycle and Top-of-Atmosphere Radiative Fluxes, Earth Space Sci., 9, e2021EA001978, https://doi.org/10.1029/2021EA001978, 2022. a, b, c
Twohy, C. H., Anderson, B. E., Ferrare, R. A., Sauter, K. E., L'Ecuyer, T. S., van den Heever, S. C., Heymsfield, A. J., Ismail, S., and Diskin, G. S.: Saharan Dust, Convective Lofting, Aerosol Enhancement Zones and Potential Impacts on Ice Nucleation in the Tropical Upper Troposphere, J. Geophys. Res.-Atmos., 122, 8833–8851, https://doi.org/10.1002/2017JD026933, 2017. a
Ueyama, R., Jensen, E. J., and Pfister, L.: Convective Influence on the Humidity and Clouds in the Tropical Tropopause Layer During Boreal Summer, J. Geophys. Res.-Atmos., 123, 7576–7593, https://doi.org/10.1029/2018JD028674, 2018. a
Ullrich, R., Hoose, C., Möhler, O., Niemand, M., Wagner, R., Höhler, K., Hiranuma, N., Saathoff, H., and Leisner, T.: A New Ice Nucleation Active Site Parameterization for Desert Dust and Soot, J. Atmos. Sci., 74, 699–717, https://doi.org/10.1175/JAS-D-16-0074.1, 2017. a
van den Heever, S., Haddad, Z., Tanelli, S., Stephens, G., Posselt, D., Kim, Y., Brown, S., Braun, S., Grant, L., Kollias, P., Luo, Z. J., Mace, G., Marinescu, P., Padmanabhan, S., Partain, P., Petersent, W., Prasanth, S., Rasmussen, K., Reising, S., and Schumacher, C. and the INCUS Mission team: The INCUS Mission, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9021, https://doi.org/10.5194/egusphere-egu22-9021, 2022. a
van Diedenhoven, B., Fridlind, A. M., Cairns, B., Ackerman, A. S., and Yorks, J. E.: Vertical variation of ice particle size in convective cloud tops, Geophys. Res. Lett., 43, 4586–4593, https://doi.org/10.1002/2016GL068548, 2016. a
van Diedenhoven, B., Ackerman, A. S., Fridlind, A. M., Cairns, B., and Riedi, J.: Global Statistics of Ice Microphysical and Optical Properties at Tops of Optically Thick Ice Clouds, J. Geophys. Res.-Atmos., 125, 1–21, https://doi.org/10.1029/2019JD031811, 2020. a
Virts, K. S., Wallace, J. M., Fu, Q., and Ackerman, T. P.: Tropical Tropopause Transition Layer Cirrus as Represented by CALIPSO Lidar Observations, J. Atmos. Sci., 67, 3113–3129, https://doi.org/10.1175/2010JAS3412.1, 2010. a
Voigt, A. and Shaw, T. A.: Impact of regional atmospheric cloud radiative changes on shifts of the extratropical jet stream in response to global warming, J. Climate, 29, 8399–8421, https://doi.org/10.1175/JCLI-D-16-0140.1, 2016. a
Voigt, A., Albern, N., and Papavasileiou, G.: The atmospheric pathway of the cloud-radiative impact on the circulation response to global warming: Important and uncertain, J. Climate, 32, 3051–3067, https://doi.org/10.1175/JCLI-D-18-0810.1, 2019. a, b, c
Voigt, A., Albern, N., Ceppi, P., Grise, K., Li, Y., and Medeiros, B.: Clouds, radiation, and atmospheric circulation in the present-day climate and under climate change, Wiley Interdisciplinary Reviews: Climate Change, 12, 1–22, https://doi.org/10.1002/wcc.694, 2021. a
Wall, C. J., Hartmann, D. L., Thieman, M. M., Smith, W. L., and Minnis, P.: The Life Cycle of Anvil Clouds and the Top-of-Atmosphere Radiation Balance over the Tropical West Pacific, J. Climate, 31, 10059–10080, https://doi.org/10.1175/JCLI-D-18-0154.1, 2018. a, b
Wall, C. J., Norris, J. R., Gasparini, B., Smith Jr., W. L., Thieman, M. M., and Sourdeval, O.: Observational Evidence that Radiative Heating Modifies the Life Cycle of Tropical Anvil Clouds, J. Climate, 33, 8621–8640, https://doi.org/10.1175/JCLI-D-20-0204.1, 2020. a, b, c
Wang, J., Fan, J., Feng, Z., Zhang, K., Roesler, E., Hillman, B., Shpund, J., Lin, W., and Xie, S.: Impact of a New Cloud Microphysics Parameterization on the Simulations of Mesoscale Convective Systems in E3SM, J. Adv. Model. Earth Syst., 13, e2021MS002628, https://doi.org/10.1029/2021MS002628, 2021. a
Waugh, D. and Hall, T.: Age of stratospheric air: theory, observations, and models, Rev. Geophys., 40, 1–26, https://doi.org/10.1029/2000RG000101, 2002. a
Wernli, H., Boettcher, M., Joos, H., Miltenberger, A. K., and Spichtinger, P.: A trajectory-based classification of ERA-Interim ice clouds in the region of the North Atlantic storm track, Geophys. Res. Lett., 43, 1–8, https://doi.org/10.1002/2016GL068922., 2016. a
Weverberg, K. V., Vogelmann, A. M., Lin, W., Luke, E. P., Cialella, A., Minnis, P., Khaiyer, M., Boer, E. R., and Jensen, M. P.: The Role of Cloud Microphysics Parameterization in the Simulation of Mesoscale Convective System Clouds and Precipitation in the Tropical Western Pacific, J. Atmos. Sci., 70, 1104–1128, https://doi.org/10.1175/JAS-D-12-0104.1, 2013. a
Williams, I. N. and Pierrehumbert, R. T.: Observational evidence against strongly stabilizing tropical cloud feedbacks, Geophys. Res. Lett., 44, 1503–1510, https://doi.org/10.1002/2016GL072202, 2017. a
Wing, A. A. and Cronin, T. W.: Self-aggregation of convection in long channel geometry, Q. J. Roy. Meteorol. Soc., 142, 1–15, https://doi.org/10.1002/qj.2628, 2016. a
Wing, A. A. and Emanuel, K. A.: Physical mechanisms controlling self-aggregation of convection in idealized numerical modeling simulations, J. Adv. Model. Earth Syst., 6, 59–74, https://doi.org/10.1002/2013MS000269, 2014. a
Wing, A. A., Reed, K. A., Satoh, M., Stevens, B., Bony, S., and Ohno, T.: Radiative–convective equilibrium model intercomparison project, Geosci. Model Dev., 11, 793–813, https://doi.org/10.5194/gmd-11-793-2018, 2018. a
Wing, A. A., Stauffer, C. L., Becker, T., Reed, K. A., Ahn, M., Arnold, N. P., Bony, S., Branson, M., Bryan, G. H., Chaboureau, J., Roode, S. R., Gayatri, K., Hohenegger, C., Hu, I., Jansson, F., Jones, T. R., Khairoutdinov, M., Kim, D., Martin, Z. K., Matsugishi, S., Medeiros, B., Miura, H., Moon, Y., Müller, S. K., Ohno, T., Popp, M., Prabhakaran, T., Randall, D., Rios‐Berrios, R., Rochetin, N., Roehrig, R., Romps, D. M., Ruppert, J. H., Satoh, M., Silvers, L. G., Singh, M. S., Stevens, B., Tomassini, L., Heerwaarden, C. C., Wang, S., and Zhao, M.: Clouds and Convective Self‐Aggregation in a Multi‐Model Ensemble of Radiative‐Convective Equilibrium Simulations, J. Adv. Model. Earth Syst., 12, e2020MS00213, https://doi.org/10.1029/2020MS002138, 2020. a, b, c, d, e
Winker, D. M., Pelon, J., Coakley, J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. L., Le Treut, H., McCormick, M. P., Mégie, G., Poole, L., Powell, K., Trepte, K., Vaughan, M. A., and Wielicki, B. A.: The Calipso Mission: A Global 3D View of Aerosols and Clouds, B. Am. Meteorol. Soc., 91, 1211–1229, https://doi.org/10.1175/2010BAMS3009.1, 2010. a, b
Wolf, V., Kuhn, T., Milz, M., Voelger, P., Krämer, M., and Rolf, C.: Arctic ice clouds over northern Sweden: microphysical properties studied with the Balloon-borne Ice Cloud particle Imager B-ICI, Atmos. Chem. Phys., 18, 17371–17386, https://doi.org/10.5194/acp-18-17371-2018, 2018. a
Wylie, D. P. and Woolf, H. M.: The Diurnal Cycle of Upper-Tropospheric Clouds Measured by GOES-VAS and the ISCCP, Mon. Weather Rev., 130, 171–179, https://doi.org/10.1175/1520-0493(2002)130<0171:TDCOUT>2.0.CO;2, 2002. a
Xie, S., Lin, W., Rasch, P. J., Ma, P.-L., Neale, R., Larson, V. E., Qian, Y., Bogenschutz, P. A., Caldwell, P., Cameron-Smith, P., Golaz, J.-C., Mahajan, S., Singh, B., Tang, Q., Wang, H., Yoon, J.-H., Zhang, K., and Zhang, Y.: Understanding Cloud and Convective Characteristics in Version 1 of the E3SM Atmosphere Model, J. Adv. Model. Earth Syst., 10, 1–27, https://doi.org/10.1029/2018MS001350, 2018. a
Yang, Q., Fu, Q., and Hu, Y.: Radiative impacts of clouds in the tropical tropopause layer, J. Geophys. Res.-Atmos., 115, 1–21, https://doi.org/10.1029/2009JD012393, 2010. a, b
Yi, B.: Diverse cloud radiative effects and global surface temperature simulations induced by different ice cloud optical property parameterizations, Sci. Rep., 12, 10539, https://doi.org/10.1038/s41598-022-14608-w, 2022. a
Yin, J. and Porporato, A.: Diurnal cloud cycle biases in climate models, Nat. Commun., 8, 2269, https://doi.org/10.1038/s41467-017-02369-4, 2017. a
Yuan, J. and Houze, R. A.: Global variability of mesoscale convective system anvil structure from A-train satellite data, J. Climate, 23, 5864–5888, https://doi.org/10.1175/2010JCLI3671.1, 2010. a, b
Zantedeschi, V., Falasca, F., Douglas, A., Strange, R., Kusner, M. J., and Watson-Parris, D.: Cumulo: A Dataset for Learning Cloud Classes, arxive, https://doi.org/10.48550/arXiv.1911.04227, 2022. a
Zelinka, M. D. and Hartmann, D. L.: Why is longwave cloud feedback positive?, J. Geophys. Res-.Atmos., 115, D16117, https://doi.org/10.1029/2010JD013817, 2010. a, b, c
Zelinka, M. D. and Hartmann, D. L.: The observed sensitivity of high clouds to mean surface temperature anomalies in the tropics, J. Geophys. Res.-Atmos., 116, 1–16, https://doi.org/10.1029/2011JD016459, 2011. a, b, c
Zelinka, M. D., Klein, S. A., Qin, Y., and Myers, T. A.: Evaluating Climate Models’ Cloud Feedbacks Against Expert Judgment, J. Geophys. Res.-Atmos., 127, e2021JD035198, https://doi.org/10.1029/2021JD035198, 2022. a
Zhang, A., Wang, Y., Zhang, Y., Weber, R. J., Song, Y., Ke, Z., and Zou, Y.: Modeling the global radiative effect of brown carbon: a potentially larger heating source in the tropical free troposphere than black carbon, Atmos. Chem. Phys., 20, 1901–1920, https://doi.org/10.5194/acp-20-1901-2020, 2020. a
Zhang, Y., Macke, A., and Albers, F.: Effect of crystal size spectrum and crystal shape on stratiform cirrus radiative forcing, Atmos. Res., 52, 59–75, https://doi.org/10.1016/S0169-8095(99)00026-5, 1999. a
Zhao, M., Golaz, J.-C., Held, I. M., Ramaswamy, V., Lin, S.-J., Ming, Y., Ginoux, P., Wyman, B., Donner, L. J., Paynter, D., and Guo, H.: Uncertainty in Model Climate Sensitivity Traced to Representations of Cumulus Precipitation Microphysics, J. Climate, 29, 543–560, https://doi.org/10.1175/JCLI-D-15-0191.1, 2016. a
Zhao, W., Peng, Y., Wang, B., Yi, B., Lin, Y., and Li, J.: Comparison of three ice cloud optical schemes in climate simulations with community atmospheric model version 5, Atmos. Res., 204, 37–53, https://doi.org/10.1016/J.ATMOSRES.2018.01.004, 2018. a
Zhou, C., Dessler, A. E., Zelinka, M. D., Yang, P., and Wang, T.: Cirrus feedback on interannual climate fluctuations, Geophys. Res. Lett., 41, 9166–9173, https://doi.org/10.1002/2014GL062095, 2014. a
Zhu, J., Penner, J. E., Garnier, A., Boucher, O., Gao, M., Song, L., Deng, J., Liu, C.-q., and Fu, P.: Decreased Aviation Leads to Increased Ice Crystal Number and a Positive Radiative Effect in Cirrus Clouds, AGU Advances, 3, e2021AV000546, https://doi.org/10.1029/2021AV000546, 2022. a
Executive editor
This article offers a wide ranging review of current understanding of the role various tropical cirrus cloud types play in the redistribution of water within the atmosphere and how they affect the changing Earth's energy balance by reflecting sunlight and preventing the escape of thermal energy to outer space. Improved understanding of these dynamics has been identified as critical for predicting whether such clouds may amplify or slow future global climate change. A clear exposition is provided of the various methods used to study tropical cirrus cloud characteristics and processes, including remote sensing, in situ measurements, modeling and laboratory work. Key questions include identifying how small-scale microphysical processes affect larger cloud structure, and how cirrus altitude and extent responds to changing radiation and thermodynamic profiles. A call is made not only for improved representations of cloud processes at finer scales, but also for a more holistic approach using a hierarchy of model detail. Such efforts would enable new knowledge obtained from studying even the smallest scales to be more readily placed within a broader context applicable to climate studies.
This article offers a wide ranging review of current understanding of the role various tropical...
Short summary
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.
Tropical cirrus clouds are essential for climate, but our understanding of these clouds is...
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