Articles | Volume 24, issue 13 
            
                
                    
            
            
            https://doi.org/10.5194/acp-24-7899-2024
                    © Author(s) 2024. 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-24-7899-2024
                    © Author(s) 2024. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
The correlation between Arctic sea ice, cloud phase and radiation using A-Train satellites
                                            Center for Climate Systems Research, Columbia University, New York, NY, USA
                                        
                                    
                                            NASA Goddard Institute for Space Studies, New York, NY, USA
                                        
                                    Olivia Pierpaoli
                                            Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
                                        
                                    
                                            NASA Goddard Institute for Space Studies, New York, NY, USA
                                        
                                    Matteo Ottaviani
                                            Terra Research Inc, Hoboken, NJ 07030, USA
                                        
                                    
                                            NASA Goddard Institute for Space Studies, New York, NY, USA
                                        
                                    
                                            Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
                                        
                                    
                                            NASA Goddard Institute for Space Studies, New York, NY, USA
                                        
                                    Zhonghai Jin
                                            NASA Goddard Institute for Space Studies, New York, NY, USA
                                        
                                    Israel Silber
                                            Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA
                                        
                                    
                                            now at: Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
                                        
                                    Related authors
McKenna W. Stanford, Ann M. Fridlind, Israel Silber, Andrew S. Ackerman, Greg Cesana, Johannes Mülmenstädt, Alain Protat, Simon Alexander, and Adrian McDonald
                                    Atmos. Chem. Phys., 23, 9037–9069, https://doi.org/10.5194/acp-23-9037-2023, https://doi.org/10.5194/acp-23-9037-2023, 2023
                                    Short summary
                                    Short summary
                                            
                                                Clouds play an important role in the Earth’s climate system as they modulate the amount of radiation that either reaches the surface or is reflected back to space. This study demonstrates an approach to robustly evaluate surface-based observations against a large-scale model. We find that the large-scale model precipitates too infrequently relative to observations, contrary to literature documentation suggesting otherwise based on satellite measurements.
                                            
                                            
                                        Israel Silber, Ann M. Fridlind, Johannes Verlinde, Andrew S. Ackerman, Grégory V. Cesana, and Daniel A. Knopf
                                    Atmos. Chem. Phys., 21, 3949–3971, https://doi.org/10.5194/acp-21-3949-2021, https://doi.org/10.5194/acp-21-3949-2021, 2021
                                    Short summary
                                    Short summary
                                            
                                                Long-term ground-based radar and sounding measurements over Alaska (Antarctica) indicate that more than 85 % (75 %) of supercooled clouds are precipitating at cloud base and that 75 % (50 %) are precipitating to the surface. Such high prevalence is reconciled with lesser spaceborne estimates by considering radar sensitivity. Results provide a strong observational constraint for polar cloud processes in large-scale models.
                                            
                                            
                                        Israel Silber, Jennifer M. Comstock, Adam K. Theisen, Michael R. Kieburtz, Zeen Zhu, and Jenni Kyrouac
                                        EGUsphere, https://doi.org/10.5194/egusphere-2025-4723, https://doi.org/10.5194/egusphere-2025-4723, 2025
                                    This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT). 
                                    Short summary
                                    Short summary
                                            
                                                We present PrecipBE, a multi-instrument precipitation event best-estimate data product developed at the Atmospheric Radiation Measurement (ARM) user facility, providing time series and tabular statistics of events, which could help advance model evaluation and cloud-process studies. We demonstrate PrecipBE utilization with a brief 30-year trend analysis using ARM Southern Great Plains (SGP) site data, suggesting shorter, less intense events, but rising annual rainfall, driven by rare extremes.
                                            
                                            
                                        Yijia Sun, Ann M. Fridlind, Israel Silber, Nicole Riemer, and Daniel A. Knopf
                                        EGUsphere, https://doi.org/10.5194/egusphere-2025-3620, https://doi.org/10.5194/egusphere-2025-3620, 2025
                                    This preprint is open for discussion and under review for Geoscientific Model Development (GMD). 
                                    Short summary
                                    Short summary
                                            
                                                The role of Arctic clouds in the regional climate remains uncertain due to insufficient understanding of the amount of liquid droplets and ice crystals present in these clouds. An aerosol-cloud model is employed to examine the role of different aerosol types and freezing parameterizations on the number of ice crystals. The choice of freezing parameterization significantly changes the number of ice crystals impacting the interpretation of the evolution and warming effect of Arctic clouds.
                                            
                                            
                                        Fan Mei, Qi Zhang, Damao Zhang, Jerome D. Fast, Gourihar Kulkarni, Mikhail S. Pekour, Christopher R. Niedek, Susanne Glienke, Israel Silber, Beat Schmid, Jason M. Tomlinson, Hardeep S. Mehta, Xena Mansoura, Zezhen Cheng, Gregory W. Vandergrift, Nurun Nahar Lata, Swarup China, and Zihua Zhu
                                    Atmos. Chem. Phys., 25, 3425–3444, https://doi.org/10.5194/acp-25-3425-2025, https://doi.org/10.5194/acp-25-3425-2025, 2025
                                    Short summary
                                    Short summary
                                            
                                                This study highlights the unique capability of the ArcticShark, an uncrewed aerial system, in measuring vertically resolved atmospheric properties. Data from 32 research flights in 2023 reveal seasonal patterns and correlations with conventional measurements. The consistency and complementarity of in situ and remote sensing methods are highlighted. The study demonstrates the ArcticShark’s versatility in bridging data gaps and improving the understanding of vertical atmospheric structures.
                                            
                                            
                                        Israel Silber, Jennifer M. Comstock, Michael R. Kieburtz, and Lynn M. Russell
                                    Earth Syst. Sci. Data, 17, 29–42, https://doi.org/10.5194/essd-17-29-2025, https://doi.org/10.5194/essd-17-29-2025, 2025
                                    Short summary
                                    Short summary
                                            
                                                We present ARMTRAJ, a set of multipurpose trajectory datasets, which augments cloud, aerosol, and boundary layer studies utilizing the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility data. ARMTRAJ data include ensemble run statistics that enhance consistency and serve as uncertainty metrics for air mass coordinates and state variables. ARMTRAJ will soon become a near real-time product that will accompany past, ongoing, and future ARM deployments.
                                            
                                            
                                        Abigail S. Williams, Jeramy L. Dedrick, Lynn M. Russell, Florian Tornow, Israel Silber, Ann M. Fridlind, Benjamin Swanson, Paul J. DeMott, Paul Zieger, and Radovan Krejci
                                    Atmos. Chem. Phys., 24, 11791–11805, https://doi.org/10.5194/acp-24-11791-2024, https://doi.org/10.5194/acp-24-11791-2024, 2024
                                    Short summary
                                    Short summary
                                            
                                                The measured aerosol size distribution modes reveal distinct properties characteristic of cold-air outbreaks in the Norwegian Arctic. We find higher sea spray number concentrations, smaller Hoppel minima, lower effective supersaturations, and accumulation-mode particle scavenging during cold-air outbreaks. These results advance our understanding of cold-air outbreak aerosol–cloud interactions in order to improve their accurate representation in models.
                                            
                                            
                                        Matteo Ottaviani, Gabriel Harris Myers, and Nan Chen
                                    Atmos. Meas. Tech., 17, 4737–4756, https://doi.org/10.5194/amt-17-4737-2024, https://doi.org/10.5194/amt-17-4737-2024, 2024
                                    Short summary
                                    Short summary
                                            
                                                We analyze simulated polarization observations over snow to investigate the capabilities of remote sensing to determine surface and atmospheric properties in snow-covered regions. Polarization measurements are demonstrated to aid in the determination of snow grain shape, ice crystal roughness, and the vertical distribution of impurities in the snow–atmosphere system, data that are critical for estimating snow albedo for use in climate models.
                                            
                                            
                                        McKenna W. Stanford, Ann M. Fridlind, Israel Silber, Andrew S. Ackerman, Greg Cesana, Johannes Mülmenstädt, Alain Protat, Simon Alexander, and Adrian McDonald
                                    Atmos. Chem. Phys., 23, 9037–9069, https://doi.org/10.5194/acp-23-9037-2023, https://doi.org/10.5194/acp-23-9037-2023, 2023
                                    Short summary
                                    Short summary
                                            
                                                Clouds play an important role in the Earth’s climate system as they modulate the amount of radiation that either reaches the surface or is reflected back to space. This study demonstrates an approach to robustly evaluate surface-based observations against a large-scale model. We find that the large-scale model precipitates too infrequently relative to observations, contrary to literature documentation suggesting otherwise based on satellite measurements.
                                            
                                            
                                        Frederic Tridon, Israel Silber, Alessandro Battaglia, Stefan Kneifel, Ann Fridlind, Petros Kalogeras, and Ranvir Dhillon
                                    Atmos. Chem. Phys., 22, 12467–12491, https://doi.org/10.5194/acp-22-12467-2022, https://doi.org/10.5194/acp-22-12467-2022, 2022
                                    Short summary
                                    Short summary
                                            
                                                The role of ice precipitation in the Earth water budget is not well known because ice particles are complex, and their formation involves intricate processes. Riming of ice crystals by supercooled water droplets is an efficient process, but little is known about its importance at high latitudes. In this work, by exploiting the deployment of an unprecedented number of remote sensing systems in Antarctica, we find that riming occurs at much lower temperatures compared with the mid-latitudes.
                                            
                                            
                                        Zhonghai Jin, Matteo Ottaviani, and Monika Sikand
                                        The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-106, https://doi.org/10.5194/tc-2022-106, 2022
                                    Revised manuscript not accepted 
                                    Short summary
                                    Short summary
                                            
                                                A rigorous treatment of the sea ice medium has been incorporated in an advanced radiative transfer model. The inherent optical properties of brine pockets and air bubbles are parameterized as a function of the vertical profile of the sea ice physical properties (temperature, salinity and density). We test the model performance using available albedo and transmittance measurements collected during the ICESCAPE and the SHEBA field campaigns.
                                            
                                            
                                        Israel Silber, Robert C. Jackson, Ann M. Fridlind, Andrew S. Ackerman, Scott Collis, Johannes Verlinde, and Jiachen Ding
                                    Geosci. Model Dev., 15, 901–927, https://doi.org/10.5194/gmd-15-901-2022, https://doi.org/10.5194/gmd-15-901-2022, 2022
                                    Short summary
                                    Short summary
                                            
                                                The Earth Model Column Collaboratory (EMC2) is an open-source ground-based (and air- or space-borne) lidar and radar simulator and subcolumn generator designed for large-scale models, in particular climate models, applicable also for high-resolution models. EMC2 emulates measurements while remaining faithful to large-scale models' physical assumptions implemented in their cloud or radiation schemes. We demonstrate the use of EMC2 to compare AWARE measurements with the NASA GISS ModelE3 and LES.
                                            
                                            
                                        Stefanie Kremser, Mike Harvey, Peter Kuma, Sean Hartery, Alexia Saint-Macary, John McGregor, Alex Schuddeboom, Marc von Hobe, Sinikka T. Lennartz, Alex Geddes, Richard Querel, Adrian McDonald, Maija Peltola, Karine Sellegri, Israel Silber, Cliff S. Law, Connor J. Flynn, Andrew Marriner, Thomas C. J. Hill, Paul J. DeMott, Carson C. Hume, Graeme Plank, Geoffrey Graham, and Simon Parsons
                                    Earth Syst. Sci. Data, 13, 3115–3153, https://doi.org/10.5194/essd-13-3115-2021, https://doi.org/10.5194/essd-13-3115-2021, 2021
                                    Short summary
                                    Short summary
                                            
                                                Aerosol–cloud interactions over the Southern Ocean are poorly understood and remain a major source of uncertainty in climate models. This study presents ship-borne measurements, collected during a 6-week voyage into the Southern Ocean in 2018, that are an important supplement to satellite-based measurements. For example, these measurements include data on low-level clouds and aerosol composition in the marine boundary layer, which can be used in climate model evaluation efforts.
                                            
                                            
                                        Tiehan Zhou, Kevin DallaSanta, Larissa Nazarenko, Gavin A. Schmidt, and Zhonghai Jin
                                    Atmos. Chem. Phys., 21, 7395–7407, https://doi.org/10.5194/acp-21-7395-2021, https://doi.org/10.5194/acp-21-7395-2021, 2021
                                    Short summary
                                    Short summary
                                            
                                                Stratospheric radiative damping increases with rising CO2. Sensitivity experiments using the one-dimensional mechanistic models of the quasi-biennial oscillation (QBO) indicate a shortening of the simulated QBO period due to the enhancing of the radiative damping. This result suggests that increasing radiative damping may play a role in determining the QBO period in a warming climate along with wave momentum flux entering the stratosphere and tropical vertical residual velocity.
                                            
                                            
                                        Israel Silber, Ann M. Fridlind, Johannes Verlinde, Andrew S. Ackerman, Grégory V. Cesana, and Daniel A. Knopf
                                    Atmos. Chem. Phys., 21, 3949–3971, https://doi.org/10.5194/acp-21-3949-2021, https://doi.org/10.5194/acp-21-3949-2021, 2021
                                    Short summary
                                    Short summary
                                            
                                                Long-term ground-based radar and sounding measurements over Alaska (Antarctica) indicate that more than 85 % (75 %) of supercooled clouds are precipitating at cloud base and that 75 % (50 %) are precipitating to the surface. Such high prevalence is reconciled with lesser spaceborne estimates by considering radar sensitivity. Results provide a strong observational constraint for polar cloud processes in large-scale models.
                                            
                                            
                                        Peter Kuma, Adrian J. McDonald, Olaf Morgenstern, Richard Querel, Israel Silber, and Connor J. Flynn
                                    Geosci. Model Dev., 14, 43–72, https://doi.org/10.5194/gmd-14-43-2021, https://doi.org/10.5194/gmd-14-43-2021, 2021
                            Cited articles
                        
                        Boeke, R. C. and Taylor, P. C.: Seasonal energy exchange in sea ice retreat regions contributes to differences in projected Arctic warming, Nat. Commun., 9, 5017, https://doi.org/10.1038/s41467-018-07061-9, 2018. a, b
                    
                
                        
                        Cesana, G. and Chepfer, H.: Evaluation of the cloud thermodynamic phase in a climate model using CALIPSO-GOCCP, J. Geophys. Res.-Atmos., 118, 7922–7937, https://doi.org/10.1002/jgrd.50376, 2013 (data available at: http://climserv.ipsl.polytechnique.fr/cfmip-obs/Calipso_goccp.html, last access: 9 July 2024). a, b, c
                    
                
                        
                        Cesana, G. and Silber, I.: The PHAse Cloud Type (PHACT) product, Zenodo [data set], https://doi.org/10.5281/zenodo.11088539, 2024. a, b, c
                    
                
                        
                        Cesana, G., Kay, J. E., Chepfer, H., English, J. M., and de Boer, G.: Ubiquitous low-level liquid-containing Arctic clouds: New observations and climate model constraints from CALIPSO-GOCCP, Geophys. Res. Lett., 39, 1–6, https://doi.org/10.1029/2012GL053385, 2012. a, b
                    
                
                        
                        Cesana, G., Chepfer, H., Winker, D., Getzewich, B., Cai, X., Jourdan, O., Mioche, G., Okamoto, H., Hagihara, Y., Noel, V., and Reverdy, M.: Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO, J. Geophys. Res., 121, 5788–5808, https://doi.org/10.1002/2015JD024334, 2016. a, b, c, d
                    
                
                        
                        Cesana, G. V., Khadir, T., Chepfer, H., and Chiriaco, M.: Southern Ocean Solar Reflection Biases in CMIP6 Models Linked to Cloud Phase and Vertical Structure Representations, Geophys. Res. Lett., 49, e2022GL099777, https://doi.org/10.1029/2022GL099777, 2022. a
                    
                
                        
                        Cesana, G., Pierpaoli, O., Vu, L., Ottaviani, M., and Li, Z.: DARDAR 1°x1° gridded statistics of cloud fraction by cloud phase type over the Arctic for the period 2007–2010, Zenodo [data set], https://doi.org/10.5281/zenodo.11088101, 2024a. a
                    
                
                        
                        Cesana, G. V., Ackerman, A. S., Fridlind, A. M., Silber, I., Del Genio, A. D., Zelinka, M. D., Chepfer, H., Khadir, T., and Roehrig, R.: Observational constraint on a feedback from supercooled clouds reduces projected warming uncertainty, Communications Earth & Environment, 5, 181, https://doi.org/10.1038/s43247-024-01339-1, 2024b. a
                    
                
                        
                        Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J. L., Minnis, P., Stubenrauch, C. J., and Zeng, S.: The GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP), J. Geophys. Res., 115, D00H16, https://doi.org/10.1029/2009JD012251, 2010. a
                    
                
                        
                        Curry, J. A., Schramm, J. L., Rossow, W. B., and Randall, D.: Overview of Arctic Cloud and Radiation Characteristics, J. Climate, 9, 1731–1764, https://doi.org/10.1175/1520-0442(1996)009<1731:OOACAR>2.0.CO;2, 1996. a
                    
                
                        
                        Delanoë, J. and Hogan, R. J.: Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds, J. Geophys. Res.-Atmos., 115, D00H29, https://doi.org/10.1029/2009JD012346, 2010. a, b
                    
                
                        
                        Dörr, J., Årthun, M., Eldevik, T., and Madonna, E.: Mechanisms of Regional Winter Sea-Ice Variability in a Warming Arctic, J. Climate, 34, 8635–8653, https://doi.org/10.1175/JCLI-D-21-0149.1, 2021. a
                    
                
                        
                        Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.6860a573, 2023. a
                    
                
                        
                        Kay, J. E. and Gettelman, A.: Cloud influence on and response to seasonal Arctic sea ice loss, J. Geophys. Res.-Atmos., 114, 1–18, https://doi.org/10.1029/2009JD011773, 2009. a, b, c
                    
                
                        
                        Kim, Y.-H., Min, S.-K., Gillett, N. P., Notz, D., and Malinina, E.: Observationally-constrained projections of an ice-free Arctic even under a low emission scenario, Nat. Commun., 14, 3139, https://doi.org/10.1038/s41467-023-38511-8, 2023. a, b
                    
                
                        
                        Lacour, A., Chepfer, H., Shupe, M. D., Miller, N. B., Noel, V., Kay, J., Turner, D. D., and Guzman, R.: Greenland Clouds Observed in CALIPSO-GOCCP: Comparison with Ground-Based Summit Observations, J. Climate, 30, 6065–6083, https://doi.org/10.1175/JCLI-D-16-0552.1, 2017. a
                    
                
                        
                        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
                    
                
                        
                        Lelli, L., Vountas, M., Khosravi, N., and Burrows, J. P.: Satellite remote sensing of regional and seasonal Arctic cooling showing a multi-decadal trend towards brighter and more liquid clouds, Atmos. Chem. Phys., 23, 2579–2611, https://doi.org/10.5194/acp-23-2579-2023, 2023. a
                    
                
                        
                        Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., and Riedi, J.: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, 2016. a, b
                    
                
                        
                        McIlhattan, E. A., L'Ecuyer, T. S., and Miller, N. B.: Observational evidence linking arctic supercooled liquid cloud biases in CESM to snowfall processes, J. Climate, 30, 4477–4495, https://doi.org/10.1175/JCLI-D-16-0666.1, 2017. a
                    
                
                        
                        Middlemas, E. A., Kay, J. E., Medeiros, B. M., and Maroon, E. A.: Quantifying the Influence of Cloud Radiative Feedbacks on Arctic Surface Warming Using Cloud Locking in an Earth System Model, Geophys. Res. Lett., 47, e2020GL089207, https://doi.org/10.1029/2020GL089207, 2020. a
                    
                
                        
                        Mioche, G., Jourdan, O., Ceccaldi, M., and Delanoë, J.: Variability of mixed-phase clouds in the Arctic with a focus on the Svalbard region: a study based on spaceborne active remote sensing, Atmos. Chem. Phys., 15, 2445–2461, https://doi.org/10.5194/acp-15-2445-2015, 2015. a, b, c
                    
                
                        
                        Overland, J. E., Adams, J. M., and Bond, N. A.: Decadal Variability of the Aleutian Low and Its Relation to High-Latitude Circulation, J. Climate, 12, 1542–1548, https://doi.org/10.1175/1520-0442(1999)012<1542:DVOTAL>2.0.CO;2, 1999. a
                    
                
                        
                        Pithan, F. and Mauritsen, T.: Arctic amplification dominated by temperature feedbacks in contemporary climate models, Nat. Geosci., 7, 181–184, https://doi.org/10.1038/ngeo2071, 2014. a
                    
                
                        
                        Pithan, F., Svensson, G., Caballero, R., Chechin, D., Cronin, T. W., Ekman, A. M. L., Neggers, R., Shupe, M. D., Solomon, A., Tjernström, M., and Wendisch, M.: Role of air-mass transformations in exchange between the Arctic and mid-latitudes, Nat. Geosci., 11, 805–812, https://doi.org/10.1038/s41561-018-0234-1, 2018. a, b
                    
                
                        
                        Shulski, M., Walsh, J., Stevens, E., and Thoman, R.: Diagnosis of extended cold-season temperature Anomalies in Alaska, Mon. Weather Rev., 138, 453–462, https://doi.org/10.1175/2009MWR3039.1, 2010. 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
                    
                
                        
                        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
                    
                
                        
                        Silber, I., Fridlind, A. M., Verlinde, J., Russell, L. M., and Ackerman, A. S.: Nonturbulent Liquid-Bearing Polar Clouds: Observed Frequency of Occurrence and Simulated Sensitivity to Gravity Waves, Geophys. Res. Lett., 47, 1–11, https://doi.org/10.1029/2020GL087099, 2020. a
                    
                
                        
                        Silber, I., Fridlind, A. M., Verlinde, J., Ackerman, A. S., Cesana, G. V., and Knopf, D. A.: The prevalence of precipitation from polar supercooled clouds, Atmos. Chem. Phys., 21, 3949–3971, https://doi.org/10.5194/acp-21-3949-2021, 2021. a
                    
                
                        
                        Sun, M., Doelling, D. R., Loeb, N. G., Scott, R. C., Wilkins, J., Nguyen, L. T., and Mlynczak, P.: Clouds and the Earth's Radiant Energy System (CERES) FluxByCldTyp Edition 4 Data Product, J. Atmos. Ocean. Tech., 39, 303–318, https://doi.org/10.1175/JTECH-D-21-0029.1, 2022 (data available at: https://ceres-tool.larc.nasa.gov/ord-tool/jsp/FluxByCldTypSelection.jsp, last access: 9 July 2024, and https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp, last access: 9 July 2024).  a, b, c, d
                    
                
                        
                        Taylor, P. C. and Monroe, E.: Isolating the Surface Type Influence on Arctic Low-Clouds, J. Geophys. Res.-Atmos., 128, e2022JD038098, https://doi.org/10.1029/2022JD038098, 2023. a, b, c
                    
                
                        
                        Walsh, J. E., Bieniek, P. A., Brettschneider, B., Euskirchen, E. S., Lader, R., and Thoman, R. L.: The exceptionally warm winter of 2015/16 in Alaska, J. Climate, 30, 2069–2088, https://doi.org/10.1175/JCLI-D-16-0473.1, 2017. a
                    
                
                        
                        Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, 1–12, https://doi.org/10.1029/2019GL085782, 2020. a
                    
                Short summary
                    Better characterizing the relationship between sea ice and clouds is key to understanding Arctic climate because clouds and sea ice affect surface radiation and modulate Arctic surface warming. Our results indicate that Arctic liquid clouds robustly increase in response to sea ice decrease. This increase has a cooling effect on the surface because more solar radiation is reflected back to space, and it should contribute to dampening future Arctic surface warming.
                    Better characterizing the relationship between sea ice and clouds is key to understanding Arctic...
                    
                Altmetrics
                
                Final-revised paper
            
            
                    Preprint
                
                     
 
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                     
                     
                    