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
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Aiko Voigt, Stefanie North, Blaž Gasparini, and Seung-Hee Ham
Atmos. Chem. Phys., 24, 9749–9775, https://doi.org/10.5194/acp-24-9749-2024, https://doi.org/10.5194/acp-24-9749-2024, 2024
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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
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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
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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.
Jasna V. Pittman, Bruce C. Daube, Steven C. Wofsy, Elliot L. Atlas, Maria A. Navarro, Eric J. Hintsa, Fred L. Moore, Geoff S. Dutton, James W. Elkins, Troy D. Thornberry, Andrew W. Rollins, Eric J. Jensen, Thaopaul Bui, Jonathan M. Dean-Day, and Leonhard Pfister
EGUsphere, https://doi.org/10.5194/egusphere-2024-3832, https://doi.org/10.5194/egusphere-2024-3832, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Wildfires emit aerosols and precursors that once in the stratosphere could initiate stratospheric ozone loss. The Airborne Tropical TRopopause EXperiment campaign sampled the western Pacific, the dominant longitudes where surface air lofted by convection enters the global stratosphere. Aircraft measurements provided evidence of persistent pollution layers of biomass burning character at these longitudes in the lower stratosphere, largely originating from distant fires over Africa and Indonesia.
Edgardo I. Sepulveda Araya, Sylvia C. Sullivan, and Aiko Voigt
EGUsphere, https://doi.org/10.5194/egusphere-2024-3212, https://doi.org/10.5194/egusphere-2024-3212, 2024
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Clouds composed of ice crystals are key when evaluating atmospheric radiation. The morphology of the crystals found in clouds is not clear yet, and even less clear is the impact on cloud heating rate, which is essential to describe precipitation and wind patterns. This motivated us to study how the heating rate behaves under a variety of ice complexity and environmental conditions, finding that increasing complexity in high and dense clouds weakens the heating rate.
Aiko Voigt, Stefanie North, Blaž Gasparini, and Seung-Hee Ham
Atmos. Chem. Phys., 24, 9749–9775, https://doi.org/10.5194/acp-24-9749-2024, https://doi.org/10.5194/acp-24-9749-2024, 2024
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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.
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
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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.
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
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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.
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)
The role of ascent timescales for warm conveyor belt (WCB) moisture transport into the upper troposphere and lower stratosphere (UTLS)
Estimating the concentration of silver iodide needed to detect unambiguous signatures of glaciogenic cloud seeding
Ice-nucleating particle concentration impacts cloud properties over Dronning Maud Land, East Antarctica, in COSMO-CLM2
Numerical simulation of aerosol concentration effects on cloud droplet size spectrum evolutions of warm stratiform clouds in Jiangxi, China
The impact of aerosol on cloud water: a heuristic perspective
The presence of clouds lowers climate sensitivity in the MPI-ESM1.2 climate model
Diurnal variation in an amplified canopy urban heat island during heat wave periods in the megacity of Beijing: roles of mountain–valley breeze and urban morphology
Diurnal evolution of non-precipitating marine stratocumuli in a large-eddy simulation ensemble
High ice water content in tropical mesoscale convective systems (a conceptual model)
Evolution of cloud droplet temperature and lifetime in spatiotemporally varying subsaturated environments with implications for ice nucleation at cloud edges
Effect of secondary ice production processes on the simulation of ice pellets using the Predicted Particle Properties microphysics scheme
Simulated particle evolution within a winter storm: contributions of riming to radar moments and precipitation fallout
A thermal-driven graupel generation process to explain dry-season convective vigor over the Amazon
Modeling homogeneous ice nucleation from drop-freezing experiments: impact of droplet volume dispersion and cooling rates
Cloud water adjustments to aerosol perturbations are buffered by solar heating in non-precipitating marine stratocumuli
Glaciation of mixed-phase clouds: insights from bulk model and bin-microphysics large-eddy simulation informed by laboratory experiment
Microphysical processes involving the vapour phase dominate in simulated low-level Arctic clouds
Understanding aerosol–cloud interactions using a single-column model for a cold-air outbreak case during the ACTIVATE campaign
On the sensitivity of aerosol–cloud interactions to changes in sea surface temperature in radiative–convective equilibrium
Exploring aerosol–cloud interactions in liquid-phase clouds over eastern China and its adjacent ocean using the WRF-Chem–SBM model
Impact of secondary ice production on thunderstorm electrification under different aerosol conditions
The impact of mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain
Finite domains cause bias in measured and modeled distributions of cloud sizes
A systematic evaluation of high-cloud controlling factors
Tracking precipitation features and associated large-scale environments over southeastern Texas
Revisiting the evolution of downhill thunderstorms over Beijing: a new perspective from a radar wind profiler mesonet
How well can persistent contrails be predicted? An update
Model analysis of biases in satellite diagnosed aerosol effect on cloud liquid water path
Dynamical imprints on precipitation cluster statistics across a hierarchy of high-resolution simulations
Potential impacts of marine fuel regulations on Arctic clouds and radiative feedbacks
Present-day correlations are insufficient to predict cloud albedo change by anthropogenic aerosols in E3SM v2
Simulations of primary and secondary ice production during an Arctic mixed-phase cloud case from the Ny-Ålesund Aerosol Cloud Experiment (NASCENT) campaign
Microphysical characteristics of precipitation within convective overshooting over East China observed by GPM DPR and ERA5
Effects of radiative cooling on advection fog over the northwest Pacific Ocean: observations and large-eddy simulations
Evaluating the Wegener–Bergeron–Findeisen process in ICON in large-eddy mode with in situ observations from the CLOUDLAB project
Aerosol-induced closure of marine cloud cells: enhanced effects in the presence of precipitation
Impact of ice multiplication on the cloud electrification of a cold-season thunderstorm: a numerical case study
Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model
Interactions between trade wind clouds and local forcings over the Great Barrier Reef: a case study using convection-permitting simulations
Variability in the properties of the distribution of the relative humidity with respect to ice: implications for contrail formation
Simulating the seeder–feeder impacts on cloud ice and precipitation over the Alps
Can pollen affect precipitation?
Cloud response to co-condensation of water and organic vapors over the boreal forest
Distribution and morphology of non-persistent contrail and persistent contrail formation areas in ERA5
Connection of Surface Snowfall Bias to Cloud Phase Bias – Satellite Observations, ERA5, and CMIP6
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
Cornelis Schwenk and Annette Miltenberger
Atmos. Chem. Phys., 24, 14073–14099, https://doi.org/10.5194/acp-24-14073-2024, https://doi.org/10.5194/acp-24-14073-2024, 2024
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Warm conveyor belts (WCBs) transport moisture into the upper atmosphere, where it acts as a greenhouse gas. This transport is not well understood, and the role of rapidly rising air is unclear. We simulate a WCB and look at fast- and slow-rising air to see how moisture is (differently) transported. We find that for fast-ascending air more ice particles reach higher into the atmosphere and that frozen cloud particles are removed differently than during slow ascent, which has more water vapour.
Jing Yang, Jiaojiao Li, Meilian Chen, Xiaoqin Jing, Yan Yin, Bart Geerts, Zhien Wang, Yubao Liu, Baojun Chen, Shaofeng Hua, Hao Hu, Xiaobo Dong, Ping Tian, Qian Chen, and Yang Gao
Atmos. Chem. Phys., 24, 13833–13848, https://doi.org/10.5194/acp-24-13833-2024, https://doi.org/10.5194/acp-24-13833-2024, 2024
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Detecting unambiguous signatures is vital for examining cloud-seeding impacts, but often, seeding signatures are immersed in natural variability. In this study, reflectivity changes induced by glaciogenic seeding using different AgI concentrations are investigated under various conditions, and a method is developed to estimate the AgI concentration needed to detect unambiguous seeding signatures. The results aid in operational seeding-based decision-making regarding the amount of AgI dispersed.
Florian Sauerland, Niels Souverijns, Anna Possner, Heike Wex, Preben Van Overmeiren, Alexander Mangold, Kwinten Van Weverberg, and Nicole van Lipzig
Atmos. Chem. Phys., 24, 13751–13768, https://doi.org/10.5194/acp-24-13751-2024, https://doi.org/10.5194/acp-24-13751-2024, 2024
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We use a regional climate model, COSMO-CLM², enhanced with a module resolving aerosol processes, to study Antarctic clouds. We prescribe different concentrations of ice-nucleating particles to our model to assess how these clouds respond to concentration changes, validating results with cloud and aerosol observations from the Princess Elisabeth Antarctica station. Our results show that aerosol–cloud interactions vary with temperature, providing valuable insights into Antarctic cloud dynamics.
Yi Li, Xiaoli Liu, and Hengjia Cai
Atmos. Chem. Phys., 24, 13525–13540, https://doi.org/10.5194/acp-24-13525-2024, https://doi.org/10.5194/acp-24-13525-2024, 2024
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The influence of different aerosol modes on cloud processes remains controversial. We modified the aerosol spectra and concentrations to simulate a warm stratiform cloud process in Jiangxi, China, using the WRF-SBM scheme. Research shows that different aerosol spectra have diverse effects on cloud droplet spectra, cloud development, and the correlation between dispersion (ε) and cloud physics quantities. Compared to cloud droplet concentration, ε is more sensitive to the volume radius.
Fabian Hoffmann, Franziska Glassmeier, and Graham Feingold
Atmos. Chem. Phys., 24, 13403–13412, https://doi.org/10.5194/acp-24-13403-2024, https://doi.org/10.5194/acp-24-13403-2024, 2024
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Clouds constitute a major cooling influence on Earth's climate system by reflecting a large fraction of the incident solar radiation back to space. This ability is controlled by the number of cloud droplets, which is governed by the number of aerosol particles in the atmosphere, laying the foundation for so-called aerosol–cloud–climate interactions. In this study, a simple model to understand the effect of aerosol on cloud water is developed and applied.
Andrea Mosso, Thomas Hocking, and Thorsten Mauritsen
Atmos. Chem. Phys., 24, 12793–12806, https://doi.org/10.5194/acp-24-12793-2024, https://doi.org/10.5194/acp-24-12793-2024, 2024
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Clouds play a crucial role in the Earth's energy balance, as they can either warm up or cool down the area they cover depending on their height and depth. They are expected to alter their behaviour under climate change, affecting the warming generated by greenhouse gases. This paper proposes a new method to estimate their overall effect on this warming by simulating a climate where clouds are transparent. Results show that with the model used, clouds have a stabilising effect on climate.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 24, 12807–12822, https://doi.org/10.5194/acp-24-12807-2024, https://doi.org/10.5194/acp-24-12807-2024, 2024
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This paper explored the formation mechanisms of the amplified canopy urban heat island intensity (ΔCUHII) during heat wave (HW) periods in the megacity of Beijing from the perspectives of mountain–valley breeze and urban morphology. During the mountain breeze phase, high-rise buildings with lower sky view factors (SVFs) had a pronounced effect on the ΔCUHII. During the valley breeze phase, high-rise buildings exerted a dual influence on the ΔCUHII.
Yao-Sheng Chen, Jianhao Zhang, Fabian Hoffmann, Takanobu Yamaguchi, Franziska Glassmeier, Xiaoli Zhou, and Graham Feingold
Atmos. Chem. Phys., 24, 12661–12685, https://doi.org/10.5194/acp-24-12661-2024, https://doi.org/10.5194/acp-24-12661-2024, 2024
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Marine stratocumulus cloud is a type of shallow cloud that covers the vast areas of Earth's surface. It plays an important role in Earth's energy balance by reflecting solar radiation back to space. We used numerical models to simulate a large number of marine stratocumuli with different characteristics. We found that how the clouds develop throughout the day is affected by the level of humidity in the air above the clouds and how closely the clouds connect to the ocean surface.
Alexei Korolev, Zhipeng Qu, Jason Milbrandt, Ivan Heckman, Mélissa Cholette, Mengistu Wolde, Cuong Nguyen, Greg M. McFarquhar, Paul Lawson, and Ann M. Fridlind
Atmos. Chem. Phys., 24, 11849–11881, https://doi.org/10.5194/acp-24-11849-2024, https://doi.org/10.5194/acp-24-11849-2024, 2024
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The phenomenon of high ice water content (HIWC) occurs in mesoscale convective systems (MCSs) when a large number of small ice particles with typical sizes of a few hundred micrometers is found at high altitudes. It was found that secondary ice production in the vicinity of the melting layer plays a key role in the formation and maintenance of HIWC. This study presents a conceptual model of the formation of HIWC in tropical MCSs based on in situ observations and numerical simulation.
Puja Roy, Robert M. Rauber, and Larry Di Girolamo
Atmos. Chem. Phys., 24, 11653–11678, https://doi.org/10.5194/acp-24-11653-2024, https://doi.org/10.5194/acp-24-11653-2024, 2024
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Cloud droplet temperature and lifetime impact cloud microphysical processes such as the activation of ice-nucleating particles. We investigate the thermal and radial evolution of supercooled cloud droplets and their surrounding environments with an aim to better understand observed enhanced ice formation at supercooled cloud edges. This analysis shows that the magnitude of droplet cooling during evaporation is greater than estimated from past studies, especially for drier environments.
Mathieu Lachapelle, Mélissa Cholette, and Julie M. Thériault
Atmos. Chem. Phys., 24, 11285–11304, https://doi.org/10.5194/acp-24-11285-2024, https://doi.org/10.5194/acp-24-11285-2024, 2024
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Hazardous precipitation types such as ice pellets and freezing rain are difficult to predict because they are associated with complex microphysical processes. Using Predicted Particle Properties (P3), this work shows that secondary ice production processes increase the amount of ice pellets simulated while decreasing the amount of freezing rain. Moreover, the properties of the simulated precipitation compare well with those that were measured.
Andrew DeLaFrance, Lynn A. McMurdie, Angela K. Rowe, and Andrew J. Heymsfield
Atmos. Chem. Phys., 24, 11191–11206, https://doi.org/10.5194/acp-24-11191-2024, https://doi.org/10.5194/acp-24-11191-2024, 2024
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Using a numerical model, the process whereby falling ice crystals accumulate supercooled liquid water droplets is investigated to elucidate its effects on radar-based measurements and surface precipitation. We demonstrate that this process accounted for 55% of the precipitation during a wintertime storm and is uniquely discernable from other ice crystal growth processes in Doppler velocity measurements. These results have implications for measurements from airborne and spaceborne platforms.
Toshi Matsui, Daniel Hernandez-Deckers, Scott E. Giangrande, Thiago S. Biscaro, Ann Fridlind, and Scott Braun
Atmos. Chem. Phys., 24, 10793–10814, https://doi.org/10.5194/acp-24-10793-2024, https://doi.org/10.5194/acp-24-10793-2024, 2024
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Using computer simulations and real measurements, we discovered that storms over the Amazon were narrower but more intense during the dry periods, producing heavier rain and more ice particles in the clouds. Our research showed that cumulus bubbles played a key role in creating these intense storms. This study can improve the representation of the effect of continental and ocean environments on tropical regions' rainfall patterns in simulations.
Ravi Kumar Reddy Addula, Ingrid de Almeida Ribeiro, Valeria Molinero, and Baron Peters
Atmos. Chem. Phys., 24, 10833–10848, https://doi.org/10.5194/acp-24-10833-2024, https://doi.org/10.5194/acp-24-10833-2024, 2024
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Ice nucleation from supercooled droplets is important in many weather and climate modeling efforts. For experiments where droplets are steadily supercooled from the freezing point, our work combines nucleation theory and survival probability analysis to predict the nucleation spectrum, i.e., droplet freezing probabilities vs. temperature. We use the new framework to extract approximately consistent rate parameters from experiments with different cooling rates and droplet sizes.
Jianhao Zhang, Yao-Sheng Chen, Takanobu Yamaguchi, and Graham Feingold
Atmos. Chem. Phys., 24, 10425–10440, https://doi.org/10.5194/acp-24-10425-2024, https://doi.org/10.5194/acp-24-10425-2024, 2024
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Quantifying cloud response to aerosol perturbations presents a major challenge in understanding the human impact on climate. Using a large number of process-resolving simulations of marine stratocumulus, we show that solar heating drives a negative feedback mechanism that buffers the persistent negative trend in cloud water adjustment after sunrise. This finding has implications for the dependence of the cloud cooling effect on the timing of deliberate aerosol perturbations.
Aaron Wang, Steve Krueger, Sisi Chen, Mikhail Ovchinnikov, Will Cantrell, and Raymond A. Shaw
Atmos. Chem. Phys., 24, 10245–10260, https://doi.org/10.5194/acp-24-10245-2024, https://doi.org/10.5194/acp-24-10245-2024, 2024
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We employ two methods to examine a laboratory experiment on clouds with both ice and liquid phases. The first assumes well-mixed properties; the second resolves the spatial distribution of turbulence and cloud particles. Results show that while the trends in mean properties generally align, when turbulence is resolved, liquid droplets are not fully depleted by ice due to incomplete mixing. This underscores the threshold of ice mass fraction in distinguishing mixed-phase clouds from ice clouds.
Theresa Kiszler, Davide Ori, and Vera Schemann
Atmos. Chem. Phys., 24, 10039–10053, https://doi.org/10.5194/acp-24-10039-2024, https://doi.org/10.5194/acp-24-10039-2024, 2024
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Microphysical processes impact the phase-partitioning of clouds. In this study we evaluate these processes while focusing on low-level Arctic clouds. To achieve this we used an extensive simulation set in combination with a new diagnostic tool. This study presents our findings on the relevance of these processes and their behaviour under different thermodynamic regimes.
Shuaiqi Tang, Hailong Wang, Xiang-Yu Li, Jingyi Chen, Armin Sorooshian, Xubin Zeng, Ewan Crosbie, Kenneth L. Thornhill, Luke D. Ziemba, and Christiane Voigt
Atmos. Chem. Phys., 24, 10073–10092, https://doi.org/10.5194/acp-24-10073-2024, https://doi.org/10.5194/acp-24-10073-2024, 2024
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We examined marine boundary layer clouds and their interactions with aerosols in the E3SM single-column model (SCM) for a case study. The SCM shows good agreement when simulating the clouds with high-resolution models. It reproduces the relationship between cloud droplet and aerosol particle number concentrations as produced in global models. However, the relationship between cloud liquid water and droplet number concentration is different, warranting further investigation.
Suf Lorian and Guy Dagan
Atmos. Chem. Phys., 24, 9323–9338, https://doi.org/10.5194/acp-24-9323-2024, https://doi.org/10.5194/acp-24-9323-2024, 2024
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We examine the combined effect of aerosols and sea surface temperature (SST) on clouds under equilibrium conditions in cloud-resolving radiative–convective equilibrium simulations. We demonstrate that the aerosol–cloud interaction's effect on top-of-atmosphere energy gain strongly depends on the underlying SST, while the shortwave part of the spectrum is significantly more sensitive to SST. Furthermore, increasing aerosols influences upper-troposphere stability and thus anvil cloud fraction.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia
Atmos. Chem. Phys., 24, 9101–9118, https://doi.org/10.5194/acp-24-9101-2024, https://doi.org/10.5194/acp-24-9101-2024, 2024
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We explore aerosol–cloud interactions in liquid-phase clouds over eastern China and its adjacent ocean in winter based on the WRF-Chem–SBM model, which couples a spectral-bin microphysics scheme and an online aerosol module. Our study highlights the differences in aerosol–cloud interactions between land and ocean and between precipitation clouds and non-precipitation clouds, and it differentiates and quantifies their underlying mechanisms.
Shiye Huang, Jing Yang, Qian Chen, Jiaojiao Li, Qilin Zhang, and Fengxia Guo
EGUsphere, https://doi.org/10.5194/egusphere-2024-2013, https://doi.org/10.5194/egusphere-2024-2013, 2024
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Aerosol and secondary ice production are both vital to charge separation in thunderstorms, but the relative importance of different SIP processes to cloud electrification under different aerosol conditions is not well understood. In this study, we show in a clean environment, the shattering of freezing drops has the greatest effect on the charging rate, while in a polluted environment, both rime splintering and the shattering of freezing drops have a significant effect on cloud electrification.
Nadja Omanovic, Brigitta Goger, and Ulrike Lohmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-1989, https://doi.org/10.5194/egusphere-2024-1989, 2024
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We evaluated the numerical weather model ICON in two horizontal resolutions with two bulk microphysics schemes over hilly and complex terrain in Switzerland and Austria, respectively. We focused on the model's ability of simulating mid-level clouds in summer and winter. By combining observational data from two different field campaigns we show that both an increase in horizontal resolution and a more advanced cloud microphysics scheme is strongly beneficial for the cloud representation.
Thomas D. DeWitt and Timothy J. Garrett
Atmos. Chem. Phys., 24, 8457–8472, https://doi.org/10.5194/acp-24-8457-2024, https://doi.org/10.5194/acp-24-8457-2024, 2024
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There is considerable disagreement on mathematical parameters that describe the number of clouds of different sizes as well as the size of the largest clouds. Both are key defining characteristics of Earth's atmosphere. A previous study provided an incorrect explanation for the disagreement. Instead, the disagreement may be explained by prior studies not properly accounting for the size of their measurement domain. We offer recommendations for how the domain size can be accounted for.
Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack
Atmos. Chem. Phys., 24, 8295–8316, https://doi.org/10.5194/acp-24-8295-2024, https://doi.org/10.5194/acp-24-8295-2024, 2024
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Aiming to inform parameter selection for future observational constraint analyses, we incorporate five candidate meteorological drivers specifically targeting high clouds into a cloud controlling factor framework within a range of spatial domain sizes. We find a discrepancy between optimal domain size for predicting locally and globally aggregated cloud radiative anomalies and identify upper-tropospheric static stability as an important high-cloud controlling factor.
Ye Liu, Yun Qian, Larry K. Berg, Zhe Feng, Jianfeng Li, Jingyi Chen, and Zhao Yang
Atmos. Chem. Phys., 24, 8165–8181, https://doi.org/10.5194/acp-24-8165-2024, https://doi.org/10.5194/acp-24-8165-2024, 2024
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Deep convection under various large-scale meteorological patterns (LSMPs) shows distinct precipitation features. In southeastern Texas, mesoscale convective systems (MCSs) contribute significantly to precipitation year-round, while isolated deep convection (IDC) is prominent in summer and fall. Self-organizing maps (SOMs) reveal convection can occur without large-scale lifting or moisture convergence. MCSs and IDC events have distinct life cycles influenced by specific LSMPs.
Xiaoran Guo, Jianping Guo, Tianmeng Chen, Ning Li, Fan Zhang, and Yuping Sun
Atmos. Chem. Phys., 24, 8067–8083, https://doi.org/10.5194/acp-24-8067-2024, https://doi.org/10.5194/acp-24-8067-2024, 2024
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The prediction of downhill thunderstorms (DSs) remains elusive. We propose an objective method to identify DSs, based on which enhanced and dissipated DSs are discriminated. A radar wind profiler (RWP) mesonet is used to derive divergence and vertical velocity. The mid-troposphere divergence and prevailing westerlies enhance the intensity of DSs, whereas low-level divergence is observed when the DS dissipates. The findings highlight the key role that an RWP mesonet plays in the evolution of DSs.
Sina Hofer, Klaus Gierens, and Susanne Rohs
Atmos. Chem. Phys., 24, 7911–7925, https://doi.org/10.5194/acp-24-7911-2024, https://doi.org/10.5194/acp-24-7911-2024, 2024
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We try to improve the forecast of ice supersaturation (ISS) and potential persistent contrails using data on dynamical quantities in addition to temperature and relative humidity in a modern kind of regression model. Although the results are improved, they are not good enough for flight routing. The origin of the problem is the strong overlap of probability densities conditioned on cases with and without ice-supersaturated regions (ISSRs) in the important range of 70–100 %.
Harri Kokkola, Juha Tonttila, Silvia Calderón, Sami Romakkaniemi, Antti Lipponen, Aapo Peräkorpi, Tero Mielonen, Edward Gryspeerdt, Timo H. Virtanen, Pekka Kolmonen, and Antti Arola
EGUsphere, https://doi.org/10.5194/egusphere-2024-1964, https://doi.org/10.5194/egusphere-2024-1964, 2024
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Understanding how atmospheric aerosols affect clouds is a scientific challenge. One question is how aerosols affects the amount cloud water. We used a cloud-scale model to study these effects on marine clouds. The study showed that variations in cloud properties and instrument noise can cause bias in satellite derived cloud water content. However, our results suggest that for similar weather conditions with well-defined aerosol concentrations, satellite data can reliably track these effects.
Claudia Christine Stephan and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2024-2020, https://doi.org/10.5194/egusphere-2024-2020, 2024
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Tropical precipitation cluster area and intensity distributions follow power laws, but the physical processes responsible for this behavior remain unknown. We analyze global simulations that realistically represent precipitation processes. We consider Earth-like planets as well as virtual planets to realize different types of large-scale dynamics. Our finding is that power laws in Earth’s precipitation cluster statistics stem from the robust power laws in Earth’s atmospheric wind field.
Luís Filipe Escusa dos Santos, Hannah C. Frostenberg, Alejandro Baró Pérez, Annica M. L. Ekman, Luisa Ickes, and Erik S. Thomson
EGUsphere, https://doi.org/10.5194/egusphere-2024-1891, https://doi.org/10.5194/egusphere-2024-1891, 2024
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The Arctic is experiencing enhanced surface warming. The observed decline in Arctic sea-ice extent is projected to lead to an increase in Arctic shipping activity which may lead to further climatic feedbacks. We investigate, using an atmospheric model and results from marine engine experiments which focused on fuel sulfur content reduction and exhaust wet scrubbing, how ship exhaust particles influence the properties of Arctic clouds. Implications for radiative surface processes are discussed.
Naser Mahfouz, Johannes Mülmenstädt, and Susannah Burrows
Atmos. Chem. Phys., 24, 7253–7260, https://doi.org/10.5194/acp-24-7253-2024, https://doi.org/10.5194/acp-24-7253-2024, 2024
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Climate models are our primary tool to probe past, present, and future climate states unlike the more recent observation record. By constructing a hypothetical model configuration, we show that present-day correlations are insufficient to predict a persistent uncertainty in climate projection (how much sun because clouds will reflect in a changing climate). We hope our result will contribute to the scholarly conversation on better utilizing observations to constrain climate uncertainties.
Britta Schäfer, Robert Oscar David, Paraskevi Georgakaki, Julie Thérèse Pasquier, Georgia Sotiropoulou, and Trude Storelvmo
Atmos. Chem. Phys., 24, 7179–7202, https://doi.org/10.5194/acp-24-7179-2024, https://doi.org/10.5194/acp-24-7179-2024, 2024
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Mixed-phase clouds, i.e., clouds consisting of ice and supercooled water, are very common in the Arctic. However, how these clouds form is often not correctly represented in standard weather models. We show that both ice crystal concentrations in the cloud and precipitation from the cloud can be improved in the model when aerosol concentrations are prescribed from observations and when more processes for ice multiplication, i.e., the production of new ice particles from existing ice, are added.
Nan Sun, Gaopeng Lu, and Yunfei Fu
Atmos. Chem. Phys., 24, 7123–7135, https://doi.org/10.5194/acp-24-7123-2024, https://doi.org/10.5194/acp-24-7123-2024, 2024
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Microphysical characteristics of convective overshooting are essential but poorly understood, and we examine them by using the latest data. (1) Convective overshooting events mainly occur over NC (Northeast China) and northern MEC (Middle and East China). (2) Radar reflectivity of convective overshooting over NC accounts for a higher proportion below the zero level, while the opposite is the case for MEC and SC (South China). (3) Droplets of convective overshooting are large but sparse.
Liu Yang, Saisai Ding, Jing-Wu Liu, and Su-Ping Zhang
Atmos. Chem. Phys., 24, 6809–6824, https://doi.org/10.5194/acp-24-6809-2024, https://doi.org/10.5194/acp-24-6809-2024, 2024
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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.
Nadja Omanovic, Sylvaine Ferrachat, Christopher Fuchs, Jan Henneberger, Anna J. Miller, Kevin Ohneiser, Fabiola Ramelli, Patric Seifert, Robert Spirig, Huiying Zhang, and Ulrike Lohmann
Atmos. Chem. Phys., 24, 6825–6844, https://doi.org/10.5194/acp-24-6825-2024, https://doi.org/10.5194/acp-24-6825-2024, 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 at middle and high latitudes.
Matthew W. Christensen, Peng Wu, Adam C. Varble, Heng Xiao, and Jerome D. Fast
Atmos. Chem. Phys., 24, 6455–6476, https://doi.org/10.5194/acp-24-6455-2024, https://doi.org/10.5194/acp-24-6455-2024, 2024
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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 is therefore prudent to account for cloud fraction changes in assessments of aerosol–cloud interactions to improve predictions of climate change.
Jing Yang, Shiye Huang, Tianqi Yang, Qilin Zhang, Yuting Deng, and Yubao Liu
Atmos. Chem. Phys., 24, 5989–6010, https://doi.org/10.5194/acp-24-5989-2024, https://doi.org/10.5194/acp-24-5989-2024, 2024
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This study contributes to filling the dearth of understanding the impacts of different secondary ice production (SIP) processes on the cloud electrification in cold-season thunderstorms. The results suggest that SIP, especially the rime-splintering process and the shattering of freezing drops, has significant impacts on the charge structure of the storm. In addition, the modeled radar composite reflectivity and flash rate are improved after implementing the SIP processes in the model.
Ulrike Proske, Sylvaine Ferrachat, and Ulrike Lohmann
Atmos. Chem. Phys., 24, 5907–5933, https://doi.org/10.5194/acp-24-5907-2024, https://doi.org/10.5194/acp-24-5907-2024, 2024
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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 mean concentrations, which saves run time and helps to discover unexpected model behaviour. We conclude that simplifications provide a new perspective for model study and development.
Wenhui Zhao, Yi Huang, Steven Siems, Michael Manton, and Daniel Harrison
Atmos. Chem. Phys., 24, 5713–5736, https://doi.org/10.5194/acp-24-5713-2024, https://doi.org/10.5194/acp-24-5713-2024, 2024
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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.
Sidiki Sanogo, Olivier Boucher, Nicolas Bellouin, Audran Borella, Kevin Wolf, and Susanne Rohs
Atmos. Chem. Phys., 24, 5495–5511, https://doi.org/10.5194/acp-24-5495-2024, https://doi.org/10.5194/acp-24-5495-2024, 2024
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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 line with RHi and temperature variability, aircraft are likely to produce more contrails with bioethanol and liquid hydrogen as fuel. The impact of this fuel change decreases with decreasing pressure levels but increases from high latitudes to the tropics.
Zane Dedekind, Ulrike Proske, Sylvaine Ferrachat, Ulrike Lohmann, and David Neubauer
Atmos. Chem. Phys., 24, 5389–5404, https://doi.org/10.5194/acp-24-5389-2024, https://doi.org/10.5194/acp-24-5389-2024, 2024
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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, with the seeder–feeder process occurring in 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.
Marje Prank, Juha Tonttila, Xiaoxia Shang, Sami Romakkaniemi, and Tomi Raatikainen
EGUsphere, https://doi.org/10.5194/egusphere-2024-876, https://doi.org/10.5194/egusphere-2024-876, 2024
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Large primary bioparticles such as pollen can be abundant in the atmosphere. In humid conditions pollens can rupture and release a large number of fine sub-pollen particles (SPPs). The paper investigates what kind of birch pollen concentrations are needed for the pollen and SPPs to start playing a noticeable role in cloud processes and alter precipitation formation. In the studied cases only the largest observed pollen concentrations were able to noticeably alter the precipitation formation.
Liine Heikkinen, Daniel G. Partridge, Sara Blichner, Wei Huang, Rahul Ranjan, Paul Bowen, Emanuele Tovazzi, Tuukka Petäjä, Claudia Mohr, and Ilona Riipinen
Atmos. Chem. Phys., 24, 5117–5147, https://doi.org/10.5194/acp-24-5117-2024, https://doi.org/10.5194/acp-24-5117-2024, 2024
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The organic vapor condensation with water vapor (co-condensation) in rising air below clouds is modeled in this work over the boreal forest because the forest air is rich in organic vapors. We show that the number of cloud droplets can increase by 20 % if considering co-condensation. The enhancements are even larger if the air contains many small, naturally produced aerosol particles. Such conditions are most frequently met in spring in the boreal forest.
Kevin Wolf, Nicolas Bellouin, and Olivier Boucher
Atmos. Chem. Phys., 24, 5009–5024, https://doi.org/10.5194/acp-24-5009-2024, https://doi.org/10.5194/acp-24-5009-2024, 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.
Franziska Hellmuth, Tim Carlsen, Anne Sophie Daloz, Robert Oscar David, and Trude Storelvmo
EGUsphere, https://doi.org/10.5194/egusphere-2024-754, https://doi.org/10.5194/egusphere-2024-754, 2024
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This article compares the occurrence of supercooled liquid-containing clouds (sLCCs) and their link to surface snowfall in CloudSat-CALIPSO, ERA5, and CMIP6 models. Significant discrepancies were found, with ERA5 and CMIP6 consistently overestimating sLCC and snowfall frequency. This bias is likely due to cloud microphysics parameterization. This conclusion has implications for accurately representing cloud phase and snowfall in future climate projections.
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.
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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