Articles | Volume 22, issue 13
https://doi.org/10.5194/acp-22-8973-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-8973-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Do Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations
Lucas J. Sterzinger
CORRESPONDING AUTHOR
Department of Land, Air and Water Resources, University of California, Davis, Davis, California, USA
Joseph Sedlar
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
NOAA/Global Monitoring Laboratory, Boulder, Colorado, USA
Heather Guy
National Centre for Atmospheric Science, Leeds, UK
School of Earth and Environment, University of Leeds, Leeds, UK
Ryan R. Neely III
National Centre for Atmospheric Science, Leeds, UK
School of Earth and Environment, University of Leeds, Leeds, UK
Adele L. Igel
Department of Land, Air and Water Resources, University of California, Davis, Davis, California, USA
Related authors
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
Short summary
Short summary
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.
Andrew Steven Martin, Heather Guy, Michael Ray Gallagher, and Ryan Reynolds Neely III
EGUsphere, https://doi.org/10.5194/egusphere-2025-6079, https://doi.org/10.5194/egusphere-2025-6079, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Matching geospatial data between datasets recorded on different coordinate systems requires choosing parameters that impact the subset of data in downstream analyses. We developed a framework to optimise the choice of parameters by maximising the mutual information between the data being compared. The optimised parameters vary spatially, and using the optimised parameters results in better comparisons between data than using fixed choices of parameters.
Nathan H. Pope and Adele L. Igel
Atmos. Chem. Phys., 25, 5433–5444, https://doi.org/10.5194/acp-25-5433-2025, https://doi.org/10.5194/acp-25-5433-2025, 2025
Short summary
Short summary
We used an atmospheric model that simulates a single column to study the sensitivity of marine fog formed through the lowering of the base of a stratus cloud to meteorology and aerosols. We found that higher aerosol concentration reduces the likelihood and duration of fog but leads to denser fog. This overall trend was caused by multiple physical mechanisms depending on conditions.
Heather Guy, Andrew S. Martin, Erik Olson, Ian M. Brooks, and Ryan R. Neely III
Atmos. Chem. Phys., 24, 11103–11114, https://doi.org/10.5194/acp-24-11103-2024, https://doi.org/10.5194/acp-24-11103-2024, 2024
Short summary
Short summary
Aerosol particles impact cloud properties which influence Greenland Ice Sheet melt. Understanding the aerosol population that interacts with clouds is important for constraining future melt. Measurements of aerosols at cloud height over Greenland are rare, and surface measurements are often used to investigate cloud–aerosol interactions. We use a tethered balloon to measure aerosols up to cloud base and show that surface measurements are often not equivalent to those just below the cloud.
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
Short summary
Short summary
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.
Adam C. Varble, Adele L. Igel, Hugh Morrison, Wojciech W. Grabowski, and Zachary J. Lebo
Atmos. Chem. Phys., 23, 13791–13808, https://doi.org/10.5194/acp-23-13791-2023, https://doi.org/10.5194/acp-23-13791-2023, 2023
Short summary
Short summary
As atmospheric particles called aerosols increase in number, the number of droplets in clouds tends to increase, which has been theorized to increase storm intensity. We critically evaluate the evidence for this theory, showing that flaws and limitations of previous studies coupled with unaddressed cloud process complexities draw it into question. We provide recommendations for future observations and modeling to overcome current uncertainties.
Gillian Young McCusker, Jutta Vüllers, Peggy Achtert, Paul Field, Jonathan J. Day, Richard Forbes, Ruth Price, Ewan O'Connor, Michael Tjernström, John Prytherch, Ryan Neely III, and Ian M. Brooks
Atmos. Chem. Phys., 23, 4819–4847, https://doi.org/10.5194/acp-23-4819-2023, https://doi.org/10.5194/acp-23-4819-2023, 2023
Short summary
Short summary
In this study, we show that recent versions of two atmospheric models – the Unified Model and Integrated Forecasting System – overestimate Arctic cloud fraction within the lower troposphere by comparison with recent remote-sensing measurements made during the Arctic Ocean 2018 expedition. The overabundance of cloud is interlinked with the modelled thermodynamic structure, with strong negative temperature biases coincident with these overestimated cloud layers.
Heather Guy, David D. Turner, Von P. Walden, Ian M. Brooks, and Ryan R. Neely
Atmos. Meas. Tech., 15, 5095–5115, https://doi.org/10.5194/amt-15-5095-2022, https://doi.org/10.5194/amt-15-5095-2022, 2022
Short summary
Short summary
Fog formation is highly sensitive to near-surface temperatures and humidity profiles. Passive remote sensing instruments can provide continuous measurements of the vertical temperature and humidity profiles and liquid water content, which can improve fog forecasts. Here we compare the performance of collocated infrared and microwave remote sensing instruments and demonstrate that the infrared instrument is especially sensitive to the onset of thin radiation fog.
Ian Boutle, Wayne Angevine, Jian-Wen Bao, Thierry Bergot, Ritthik Bhattacharya, Andreas Bott, Leo Ducongé, Richard Forbes, Tobias Goecke, Evelyn Grell, Adrian Hill, Adele L. Igel, Innocent Kudzotsa, Christine Lac, Bjorn Maronga, Sami Romakkaniemi, Juerg Schmidli, Johannes Schwenkel, Gert-Jan Steeneveld, and Benoît Vié
Atmos. Chem. Phys., 22, 319–333, https://doi.org/10.5194/acp-22-319-2022, https://doi.org/10.5194/acp-22-319-2022, 2022
Short summary
Short summary
Fog forecasting is one of the biggest problems for numerical weather prediction. By comparing many models used for fog forecasting with others used for fog research, we hoped to help guide forecast improvements. We show some key processes that, if improved, will help improve fog forecasting, such as how water is deposited on the ground. We also showed that research models were not themselves a suitable baseline for comparison, and we discuss what future observations are required to improve them.
Heather Guy, Ian M. Brooks, Ken S. Carslaw, Benjamin J. Murray, Von P. Walden, Matthew D. Shupe, Claire Pettersen, David D. Turner, Christopher J. Cox, William D. Neff, Ralf Bennartz, and Ryan R. Neely III
Atmos. Chem. Phys., 21, 15351–15374, https://doi.org/10.5194/acp-21-15351-2021, https://doi.org/10.5194/acp-21-15351-2021, 2021
Short summary
Short summary
We present the first full year of surface aerosol number concentration measurements from the central Greenland Ice Sheet. Aerosol concentrations here have a distinct seasonal cycle from those at lower-altitude Arctic sites, which is driven by large-scale atmospheric circulation. Our results can be used to help understand the role aerosols might play in Greenland surface melt through the modification of cloud properties. This is crucial in a rapidly changing region where observations are sparse.
Joseph Sedlar, Adele Igel, and Hagen Telg
Atmos. Chem. Phys., 21, 4149–4167, https://doi.org/10.5194/acp-21-4149-2021, https://doi.org/10.5194/acp-21-4149-2021, 2021
Ines Bulatovic, Adele L. Igel, Caroline Leck, Jost Heintzenberg, Ilona Riipinen, and Annica M. L. Ekman
Atmos. Chem. Phys., 21, 3871–3897, https://doi.org/10.5194/acp-21-3871-2021, https://doi.org/10.5194/acp-21-3871-2021, 2021
Short summary
Short summary
We use detailed numerical modelling to show that small aerosol particles (diameters ~25–80 nm; so-called Aitken mode particles) significantly influence low-level cloud properties in the clean summertime high Arctic. The small particles can help sustain clouds when the concentration of larger particles is low (<10–20 cm-3). Measurements from four different observational campaigns in the high Arctic support the modelling results as they indicate that Aitken mode aerosols are frequently activated.
Maryna Lukach, David Dufton, Jonathan Crosier, Joshua M. Hampton, Lindsay Bennett, and Ryan R. Neely III
Atmos. Meas. Tech., 14, 1075–1098, https://doi.org/10.5194/amt-14-1075-2021, https://doi.org/10.5194/amt-14-1075-2021, 2021
Short summary
Short summary
This paper presents a novel technique of data-driven hydrometeor classification (HC) from quasi-vertical profiles, where the hydrometeor types are identified from an optimal number of hierarchical clusters, obtained recursively. This data-driven HC approach is capable of providing an optimal number of classes from dual-polarimetric weather radar observations. The embedded flexibility in the extent of granularity is the main advantage of this technique.
Jutta Vüllers, Peggy Achtert, Ian M. Brooks, Michael Tjernström, John Prytherch, Annika Burzik, and Ryan Neely III
Atmos. Chem. Phys., 21, 289–314, https://doi.org/10.5194/acp-21-289-2021, https://doi.org/10.5194/acp-21-289-2021, 2021
Short summary
Short summary
This paper provides interesting new results on the thermodynamic structure of the boundary layer, cloud conditions, and fog characteristics in the Arctic during the Arctic Ocean 2018 campaign. It provides information for interpreting further process studies on aerosol–cloud interactions and shows substantial differences in thermodynamic conditions and cloud characteristics based on comparison with previous campaigns. This certainly raises the question of whether it is just an exceptional year.
Cited articles
Bergeron, T.:
On the physics of clouds and precipitation, Proc. 5th Assembly U. G. G. I., Lisbon, Portugal, 1935, pp. 156–180, https://ci.nii.ac.jp/naid/10024028214/ (last access: 14 January 2022), 1935. a
Bharadwaj, N.:
Millimeter Wavelength Cloud Radar (MMCRMOM), Department of Energy Atmospheric Radiation Measurement user facility [data set], https://doi.org/10.5439/1025228, 2010. a
Birch, C. E., Brooks, I. M., Tjernström, M., Shupe, M. D., Mauritsen, T., Sedlar, J., Lock, A. P., Earnshaw, P., Persson, P. O. G., Milton, S. F., and Leck, C.:
Modelling atmospheric structure, cloud and their response to CCN in the central Arctic: ASCOS case studies, Atmos. Chem. Phys., 12, 3419–3435, https://doi.org/10.5194/acp-12-3419-2012, 2012. a
Birmili, W., Stratmann, F., and Wiedensohler, A.:
Design of a DMA-based Size Spectrometer for a Large Particle Size Sange and Stable Operation, J. Aerosol Sci., 30, 549–553, https://doi.org/10.1016/S0021-8502(98)00047-0, 1999. a
Brooks, I. M., Tjernström, M., Persson, P. O. G., Shupe, M. D., Atkinson, R. A., Canut, G., Birch, C. E., Mauritsen, T., Sedlar, J., and Brooks, B. J.:
The Turbulent Structure of the Arctic Summer Boundary Layer During The Arctic Summer Cloud-Ocean Study, J. Geophys. Res.-Atmos., 122, 9685–9704, https://doi.org/10.1002/2017JD027234, 2017. a, b
Cadeddu, M.:
Microwave Radiometer (MWRLOS), Department of Energy Atmospheric Radiation Measurement user facility [data set], https://doi.org/10.5439/1046211, 2010. a
Chandrakar, K. K., Cantrell, W., Ciochetto, D., Karki, S., Kinney, G., and Shaw, R. A.:
Aerosol Removal and Cloud Collapse Accelerated by Supersaturation Fluctuations in Turbulence, Geophys. Res. Lett., 44, 4359–4367, https://doi.org/10.1002/2017GL072762, 2017. a
Clothiaux, E. E., Ackerman, T. P., Mace, G. G., Moran, K. P., Marchand, R. T., Miller, M. A., and Martner, B. E.:
Objective Determination of Cloud Heights and Radar Reflectivities Using a Combination of Active Remote Sensors at the ARM CART Sites, J. Appl. Meteorol. Clim., 39, 645–665, https://doi.org/10.1175/1520-0450(2000)039<0645:ODOCHA>2.0.CO;2, 2000. a
Cohen, J., Screen, J. A., Furtado, J. C., Barlow, M., Whittleston, D., Coumou, D., Francis, J., Dethloff, K., Entekhabi, D., Overland, J., and Jones, J.:
Recent Arctic amplification and extreme mid-latitude weather, Nat. Geosci., 7, 627–637, https://doi.org/10.1038/NGEO2234, 2014. a
Cotton, W. R., Stevens, B., Feingold, G., and Walko, R. L.:
Large eddy simulation of marine stratocumulus cloud with explicit microphysics, in: proceedings of a workshop on parameterization of the cloud topped boundary layer, ECMWF, Reading RG29AX, UK, vol. 236, 1992. a
Cotton, W. R., Pielke Sr., R. A., Walko, R. L., Liston, G. E., Tremback, C. J., Jiang, H., McAnelly, R. L., Harrington, J. Y., Nicholls, M. E., Carrio, G. G., and McFadden, J. P.:
RAMS 2001: Current status and future directions, Meteorol. Atmos. Phys., 82, 5–29, https://doi.org/10.1007/s00703-001-0584-9, 2003. a
Deardoff, J. W.: Stratocumulus-capped mixed layers derived from a three-dimensional
model, Bound.-Lay. Meteorol., 18, 495–527, https://link.springer.com/article/10.1007/BF00119502 (last access: 14 January 2022), 1980. a
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D., Twohy, C. H., Richardson, M. S., Eidhammer, T., and Rogers, D. C.:
Predicting global atmospheric ice nuclei distributions and their impacts on climate, P. Natl. Acad. Sci. USA, 107, 11217–11222, https://doi.org/10.1073/pnas.0910818107, 2010. a, b
Devasthale, A., Sedlar, J., and Tjernström, M.:
Characteristics of water-vapour inversions observed over the Arctic by Atmospheric Infrared Sounder (AIRS) and radiosondes, Atmos. Chem. Phys., 11, 9813–9823, https://doi.org/10.5194/acp-11-9813-2011, 2011. a
Egerer, U., Ehrlich, A., Gottschalk, M., Griesche, H., Neggers, R. A. J., Siebert, H., and Wendisch, M.:
Case study of a humidity layer above Arctic stratocumulus and potential turbulent coupling with the cloud top, Atmos. Chem. Phys., 21, 6347–6364, https://doi.org/10.5194/acp-21-6347-2021, 2021. a
European Centre for Medium-Range Weather Forecasts: ERA5 Reanalysis (0.25 Degree Latitude-Longitude Grid), Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/P8GT-0R61, 2019. a
Findeisen, W.:
Kolloid-meteorologische Vorgänge bei Neiderschlags-bildung, Meteor. Z, 55, 121–133, 1938. a
Garrett, T. J. and Zhao, C.:
Increased Arctic cloud longwave emissivity associated with pollution from mid-latitudes, Nature, 440, 787–789, https://doi.org/10.1038/nature04636, 2006. a
Garrett, T. J., Radke, L. F., and Hobbs, P. V.:
Aerosol Effects on Cloud Emissivity and Surface Longwave Heating in the Arctic, J. Atmos. Sci., 59, 769–778, https://doi.org/10.1175/1520-0469(2002)059<0769:AEOCEA>2.0.CO;2, 2002. a
Gaustad, K.:
MWR Retrievals with MWRRET Version 2 (MWRRET2TURN), Department of Energy Atmospheric Radiation Measurement user facility [data set] https://doi.org/10.5439/1566156, 2014. a
Guy, H., Neely III, R. R., and Brooks, I. M.:
ICECAPS-ACE: Surface Aerosol Concentration Measurements (Condensation Nuclei > 5 nm Diameter) Taken at Summit Station Greenland, Centre for Environmental Data Analysis [data set], https://catalogue.ceda.ac.uk/uuid/f56980457ce240ccab5ac6d403c81e7a (last access: 14 January 2022), 2020. a
Guy, H., Brooks, I. M., Carslaw, K. S., Murray, B. J., Walden, V. P., Shupe, M. D., Pettersen, C., Turner, D. D., Cox, C. J., Neff, W. D., Bennartz, R., and Neely III, R. R.:
Controls on surface aerosol particle number concentrations and aerosol-limited cloud regimes over the central Greenland Ice Sheet, Atmos. Chem. Phys., 21, 15351–15374, https://doi.org/10.5194/acp-21-15351-2021, 2021. a, b, c, d, e
Harrington, J. Y.:
The Effects of Radiative and Microphysical Processes on Simulated Warm and Transition Season Arctic Stratus, PhD thesis, Colorado State University, Fort Collins, CO, USA, 1997. a
Holland, M. M. and Bitz, C. M.:
Polar amplification of climate change in coupled models, Clim. Dynam., 21, 221–232, https://doi.org/10.1007/s00382-003-0332-6, 2003. a
Intrieri, J. M., Fairall, C. W., Shupe, M. D., Persson, P. O. G., Andreas, E. L., Guest, P. S., and Moritz, R. E.:
An Annual Cycle of Arctic Surface Cloud Forcing at SHEBA, J. Geophys. Res.-Oceans, 107, SHE 13-1–SHE 13-14, https://doi.org/10.1029/2000JC000439, 2002. a, b
Jiang, H. and Feingold, G.:
Effect of aerosol on warm convective clouds: Aerosol-cloud-surface flux feedbacks in a new coupled large eddy model, J. Geophys. Res.-Atmos., 111, D01202, https://doi.org/10.1029/2005JD006138, 2006. a
Jiang, H., Feingold, G., Cotton, W. R., and Duynkerke, P. G.:
Large-eddy simulations of entrainment of cloud condensation nuclei into the Arctic boundary layer: May 18, 1998, FIRE/SHEBA case study, J. Geophys. Res.-Atmos., 106, 15113–15122, https://doi.org/10.1029/2000JD900303, 2001. a
Jung, C. H., Yoon, Y. J., Kang, H. J., Gim, Y., Lee, B. Y., Ström, J., Krejci, R., and Tunved, P.:
The Seasonal Characteristics of Cloud Condensation Nuclei (CCN) in the Arctic Lower Troposphere, Tellus B, 70, 1–13, https://doi.org/10.1080/16000889.2018.1513291, 2018. a
Klein, S. A., McCoy, R. B., Morrison, H., Ackerman, A. S., Avramov, A., de Boer, G., Chen, M., Cole, J. N., del Genio, A. D., Falk, M., Foster, M. J., Fridlind, A., Golaz, J. C., Hashino, T., Harrington, J. Y., Hoose, C., Khairoutdinov, M. F., Larson, V. E., Liu, X., Luo, Y., McFarquhar, G. M., Menon, S., Neggers, R. A., Park, S., Poellot, M. R., Schmidt, J. M., Sednev, I., Shipway, B. J., Shupe, M. D., Spangenberg, D. A., Sud, Y. C., Turner, D. D., Veron, D. E., von Salzen, K., Walker, G. K., Wang, Z., Wolf, A. B., Xie, S., Xu, K. M., Yang, F., and Zhang, G.:
Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. I: Single-layer cloud, Q. J. Roy. Meteor. Soc., 135, 979–1002, https://doi.org/10.1002/qj.416, 2009. a
Korolev, A.:
Limitations of the Wegener–Bergeron–Findeisen Mechanism in the Evolution of Mixed-Phase Clouds, J. Atmos. Sci., 64, 3372–3375, https://doi.org/10.1175/JAS4035.1, 2007. a
Kuang, C., Salwen, C., Boyer, M., and Singh, A.:
Condensation Particle Counter (AOSCPCU), ARM [data set], https://doi.org/10.5439/1046186, 2016. a
Lindenmaier, I. A., Bharadwaj, N., Johnson, K., Nelson, D., Isom, B., Hardin, J., Matthews, A., Wendler, T., and Castro, V.:
Ka ARM Zenith Radar (KAZRGE), Department of Energy Atmospheric Radiation Measurement user facility [data set], https://doi.org/10.5439/1025214, 2015. a
Lindsay, R. W. and Rothrock, D. A.:
Arctic Sea Ice Albedo from AVHRR, J. Climate, 7, 1737–1749, https://doi.org/10.1175/1520-0442(1994)007<1737:ASIAFA>2.0.CO;2, 1994. a
Loewe, K., Ekman, A. M. L., Paukert, M., Sedlar, J., Tjernström, M., and Hoose, C.:
Modelling micro- and macrophysical contributors to the dissipation of an Arctic mixed-phase cloud during the Arctic Summer Cloud Ocean Study (ASCOS), Atmos. Chem. Phys., 17, 6693–6704, https://doi.org/10.5194/acp-17-6693-2017, 2017. a, b, c
Meyers, M. P., Walko, R. L., Harrington, J. Y., and Cotton, W. R.:
New RAMS cloud microphysics parameterization. Part II: The two-moment scheme, Atmos. Res., 45, 3–39, 1997. a
Morrison, H., McCoy, R. B., Klein, S. A., Xie, S., Luo, Y., Avramov, A., Chen, M., Cole, J. N. S., Falk, M., Foster, M. J., Del Genio, A. D., Harrington, J. Y., Hoose, C., Khairoutdinov, M. F., Larson, V. E., Liu, X., McFarquhar, G. M., Poellot, M. R., von Salzen, K., Shipway, B. J., Shupe, M. D., Sud, Y. C., Turner, D. D., Veron, D. E., Walker, G. K., Wang, Z., Wolf, A. B., Xu, K.-M., Yang, F., and Zhang, G.:
Intercomparison of Model Simulations of Mixed-Phase Clouds Observed during the ARM Mixed-Phase Arctic Cloud Experiment. II: Multilayer Cloud, Q. J. Roy. Meteor. Soc., 135, 1003–1019, https://doi.org/10.1002/qj.415, 2009. a
Morrison, H., Zuidema, P., Ackerman, A. S., Avramov, A., De Boer, G., Fan, J., Fridlind, A. M., Hashino, T., Harrington, J. Y., Luo, Y., Ovchinnikov, M., and Shipway, B.:
Intercomparison of cloud model simulations of Arctic mixed-phase boundary layer clouds observed during SHEBA/FIRE-ACE, J. Adv. Model. Earth Sy., 3, M05001, https://doi.org/10.1029/2011MS000066, 2011. a
Morrison, H., De Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and Sulia, K.:
Resilience of persistent Arctic mixed-phase clouds, Nat. Geosci., 5, 11–17, https://doi.org/10.1038/ngeo1332, 2012. a, b, c, d
Naakka, T., Nygård, T., and Vihma, T.:
Arctic Humidity Inversions: Climatology and Processes, J. Climate, 31, 3765–3787, https://doi.org/10.1175/JCLI-D-17-0497.1, 2018. a
Previdi, M., Smith, K. L., and Polvani, L. M.:
Arctic Amplification of Climate Change: A Review of Underlying Mechanisms, Environ. Res. Lett., 16, 093003, https://doi.org/10.1088/1748-9326/ac1c29, 2021. a
Pruppacher, H. and Klett, J.:
Microphysics of Clouds and Precipitation, Kluwer Academic Publishing, Dordrecht, 1997. a
Saleeby, S. M. and Cotton, W. R.:
A Large-Droplet Mode and Prognostic Number Concentration of Cloud Droplets in the Colorado State University Regional Atmospheric Modeling System (RAMS). Part I: Module Descriptions and Supercell Test Simulations, J. Appl. Meteorol., 43, 182–195, https://doi.org/10.1175/1520-0450(2004)043<0182:ALMAPN>2.0.CO;2, 2004. a, b
Savre, J. and Ekman, A. M. L.:
A Theory-Based Parameterization for Heterogeneous Ice Nucleation and Implications for the Simulation of Ice Processes in Atmospheric Models: A CNT-BASED ICE NUCLEATION MODEL, J. Geophys. Res.-Atmos., 120, 4937–4961, https://doi.org/10.1002/2014JD023000, 2015. a
Schmale, J., Zieger, P., and Ekman, A. M. L.:
Aerosols in Current and Future Arctic Climate, Nat. Clim. Change, 11, 95–105, https://doi.org/10.1038/s41558-020-00969-5, 2021. a
Sedlar, J. and Tjernström, M.:
Stratiform Cloud—Inversion Characterization During the Arctic Melt Season, Bound.-Lay. Meteorol., 132, 455–474, https://doi.org/10.1007/s10546-009-9407-1, 2009. a
Sedlar, J., Tjernström, M., Mauritsen, T., Shupe, M. D., Brooks, I. M., Persson, P. O. G., Birch, C. E., Leck, C., Sirevaag, A., and Nicolaus, M.:
A transitioning Arctic surface energy budget: The impacts of solar zenith angle, surface albedo and cloud radiative forcing, Clim. Dynam., 37, 1643–1660, https://doi.org/10.1007/s00382-010-0937-5, 2011. a, b
Sedlar, J., Shupe, M. D., and Tjernström, M.:
On the Relationship between Thermodynamic Structure and Cloud Top, and Its Climate Significance in the Arctic, J. Climate, 25, 2374–2393, https://doi.org/10.1175/JCLI-D-11-00186.1, 2012. a, b, c
Sedlar, J., Igel, A., and Telg, H.:
Processes contributing to cloud dissipation and formation events on the North Slope of Alaska, Atmos. Chem. Phys., 21, 4149–4167, https://doi.org/10.5194/acp-21-4149-2021, 2021. a, b
Serreze, M. C. and Barry, R. G.:
Processes and impacts of Arctic amplification: A research synthesis, Global Planet. Change, 77, 85–96, https://doi.org/10.1016/j.gloplacha.2011.03.004, 2011. a
Shupe, M. D.:
Clouds at Arctic Atmospheric Observatories. Part II: Thermodynamic Phase Characteristics, J. Appl. Meteorol. Clim., 50, 645–661, https://doi.org/10.1175/2010JAMC2468.1, 2011. a, b
Shupe, M. D. and Intrieri, J. M.:
Cloud Radiative Forcing of the Arctic Surface: The Influence of Cloud Properties, Surface Albedo, and Solar Zenith Angle, J. Climate, 17, 616–628, https://doi.org/10.1175/1520-0442(2004)017<0616:CRFOTA>2.0.CO;2, 2004. a, b
Shupe, M. D., Matrosov, S. Y., and Uttal, T.:
Arctic Mixed-Phase Cloud Properties Derived from Surface-Based Sensors at SHEBA, J. Atmos. Sci., 63, 697–711, https://doi.org/10.1175/JAS3659.1, 2006. a
Shupe, M. D., Persson, P. O. G., Brooks, I. M., Tjernström, M., Sedlar, J., Mauritsen, T., Sjogren, S., and Leck, C.:
Cloud and boundary layer interactions over the Arctic sea ice in late summer, Atmos. Chem. Phys., 13, 9379–9399, https://doi.org/10.5194/acp-13-9379-2013, 2013a. a
Shupe, M. D., Turner, D. D., Walden, V. P., Bennartz, R., Cadeddu, M. P., Castellani, B. B., Cox, C. J., Hudak, D. R., Kulie, M. S., Miller, N. B., Neely, R. R., Neff, W. D., and Rowe, P. M.:
High and Dry: New Observations of Tropospheric and Cloud Properties above the Greenland Ice Sheet, B. Am. Meteorol. Soc., 94, 169–186, https://doi.org/10.1175/BAMS-D-11-00249.1, 2013b. a
Solomon, A., Shupe, M. D., Persson, P. O. G., and Morrison, H.:
Moisture and dynamical interactions maintaining decoupled Arctic mixed-phase stratocumulus in the presence of a humidity inversion, Atmos. Chem. Phys., 11, 10127–10148, https://doi.org/10.5194/acp-11-10127-2011, 2011. a
Sotiropoulou, G., Sedlar, J., Tjernström, M., Shupe, M. D., Brooks, I. M., and Persson, P. O. G.:
The thermodynamic structure of summer Arctic stratocumulus and the dynamic coupling to the surface, Atmos. Chem. Phys., 14, 12573–12592, https://doi.org/10.5194/acp-14-12573-2014, 2014. a
Sotiropoulou, G., Sedlar, J., Forbes, R., and Tjernstrom, M.:
Summer Arctic Clouds in the ECMWF Forecast Model: An Evaluation of Cloud Parametrization Schemes, Q. J. Roy. Meteor. Soc., 142, 387–400, https://doi.org/10.1002/qj.2658, 2016. a
Sotiropoulou, G., Bossioli, E., and Tombrou, M.:
Modeling Extreme Warm-Air Advection in the Arctic: The Role of Microphysical Treatment of Cloud Droplet Concentration, J. Geophys. Res.-Atmos., 124, 3492–3519, https://doi.org/10.1029/2018JD029252, 2019. a
Sterzinger, L.:
lsterzinger/Arctic-Rams-6.1.22: Publish on Zenodo, Zenodo [data set], https://doi.org/10.5281/zenodo.6418998, 2022a. a
Sterzinger, L.:
Model Data and Namelists for Sterzinger et al. (2022) – “Do Arctic Mixed-Phase Clouds Sometimes Dissipate Due to Insufficient Aerosol? Evidence from Comparisons between Observations and Idealized Simulations”, Zenodo [data set], https://doi.org/10.5281/zenodo.6600103, 2022b.
a
Sterzinger, L.:
Plotting Scripts for Sterzinger et al. – “Do Arctic Mixed-Phase Clouds Sometimes Dissipate Due to Insufficient Aerosol? Evidence from Comparisons between Observations and Idealized Simulations”, Zenodo [code], https://doi.org/10.5281/zenodo.6599840, 2022c. a
Stevens, R. G., Loewe, K., Dearden, C., Dimitrelos, A., Possner, A., Eirund, G. K., Raatikainen, T., Hill, A. A., Shipway, B. J., Wilkinson, J., Romakkaniemi, S., Tonttila, J., Laaksonen, A., Korhonen, H., Connolly, P., Lohmann, U., Hoose, C., Ekman, A. M. L., Carslaw, K. S., and Field, P. R.:
A model intercomparison of CCN-limited tenuous clouds in the high Arctic, Atmos. Chem. Phys., 18, 11041–11071, https://doi.org/10.5194/acp-18-11041-2018, 2018. a, b, c, d
Tjernström, M., Leck, C., Birch, C. E., Bottenheim, J. W., Brooks, B. J., Brooks, I. M., Bäcklin, L., Chang, R. Y.-W., de Leeuw, G., Di Liberto, L., de la Rosa, S., Granath, E., Graus, M., Hansel, A., Heintzenberg, J., Held, A., Hind, A., Johnston, P., Knulst, J., Martin, M., Matrai, P. A., Mauritsen, T., Müller, M., Norris, S. J., Orellana, M. V., Orsini, D. A., Paatero, J., Persson, P. O. G., Gao, Q., Rauschenberg, C., Ristovski, Z., Sedlar, J., Shupe, M. D., Sierau, B., Sirevaag, A., Sjogren, S., Stetzer, O., Swietlicki, E., Szczodrak, M., Vaattovaara, P., Wahlberg, N., Westberg, M., and Wheeler, C. R.:
The Arctic Summer Cloud Ocean Study (ASCOS): overview and experimental design, Atmos. Chem. Phys., 14, 2823–2869, https://doi.org/10.5194/acp-14-2823-2014, 2014. a, b, c
Tong, S.:
Impacts of Aerosol Concentration on the Dissipation of Arctic Mixed-Phase Clouds, Master's thesis, University of California, Davis, https://search.proquest.com/dissertations/docview/2386391281/DC2BC7F1CA7A431BPQ/1 (last access: 14 January 2022), proQuest Dissertations & Theses, 2019. a
Twomey, S.:
The Influence of Pollution on the Shortwave Albedo of Clouds, J. Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2, 1977. a
Walko, R., Cotton, W., Meyers, M., and Harrington, J.:
New RAMS cloud microphysics parameterization part I: the single-moment scheme, Atmos. Res., 38, 29–62, https://doi.org/10.1016/0169-8095(94)00087-T, 1995. a
Wegener, A.:
Thermodynamik der atmosphäre, J. A. Barth, Leipzig, Germany, google-Books-ID: kWtUAAAAMAAJ, 1911. a
Westwater, E. R., Han, Y., Shupe, M. D., and Matrosov, S. Y.:
Analysis of Integrated Cloud Liquid and Precipitable Water Vapor Retrievals from Microwave Radiometers during the Surface Heat Budget of the Arctic Ocean Project, J. Geophys. Res.-Atmos., 106, 32019–32030, https://doi.org/10.1029/2000JD000055, 2001. a
Williams, A. S. and Igel, A. L.:
Cloud Top Radiative Cooling Rate Drives Non-Precipitating Stratiform Cloud Responses to Aerosol Concentration, Geophys. Res. Lett., 48, e2021GL094740, https://doi.org/10.1029/2021GL094740, 2021. a
Short summary
Aerosol particles are required for cloud droplets to form, and the Arctic atmosphere often has much fewer aerosols than at lower latitudes. In this study, we investigate whether aerosol concentrations can drop so low as to no longer support a cloud. We use observations to initialize idealized model simulations to investigate a worst-case scenario where all aerosol is removed from the environment instantaneously. We find that this mechanism is possible in two cases and is unlikely in the third.
Aerosol particles are required for cloud droplets to form, and the Arctic atmosphere often has...
Altmetrics
Final-revised paper
Preprint