Articles | Volume 23, issue 23
https://doi.org/10.5194/acp-23-14691-2023
© Author(s) 2023. 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-23-14691-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Nov 2023
Research article |
| 29 Nov 2023
| 29 Nov 2023
Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model
Zhangcheng Pei
CORRESPONDING AUTHOR
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Australian Research Council Centre of Excellence for Climate Extremes, University of Tasmania, Hobart, Australia
College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China
Sonya L. Fiddes
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Australian Research Council Centre of Excellence for Climate Extremes, University of Tasmania, Hobart, Australia
W. John R. French
Australian Antarctic Division, Kingston TAS, Australia
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Simon P. Alexander
Australian Antarctic Division, Kingston TAS, Australia
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Marc D. Mallet
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Peter Kuma
Department of Meteorology, Stockholm University, Stockholm, Sweden
Adrian McDonald
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa / New Zealand
Related authors
No articles found.
Beth Dingley, James A. Anstey, Marta Abalos, Carsten Abraham, Tommi Bergman, Lisa Bock, Sonya Fiddes, Birgit Hassler, Ryan J. Kramer, Fei Luo, Fiona M. O'Connor, Petr Šácha, Isla R. Simpson, Laura J. Wilcox, and Mark D. Zelinka
EGUsphere, https://doi.org/10.5194/egusphere-2025-3189, https://doi.org/10.5194/egusphere-2025-3189, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This manuscript defines as a list of variables and scientific opportunities which are requested from the CMIP7 Assessment Fast Track to address open atmospheric science questions. The list reflects the output of a large public community engagement effort, coordinated across autumn 2025 through to summer 2025.
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
Short summary
Short summary
The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Sonya L. Fiddes, Matthew T. Woodhouse, Marc D. Mallet, Liam Lamprey, Ruhi S. Humphries, Alain Protat, Simon P. Alexander, Hakase Hayashida, Samuel G. Putland, Branka Miljevic, and Robyn Schofield
EGUsphere, https://doi.org/10.5194/egusphere-2024-3125, https://doi.org/10.5194/egusphere-2024-3125, 2024
Short summary
Short summary
The interaction between natural marine aerosols, clouds and radiation in the Southern Ocean is a major source of uncertainty in climate models. We evaluate the Australian climate model using aerosol observations and find it underestimates aerosol number often by over 50 %. Model changes were tested to improve aerosol concentrations, but some of our changes had severe negative effects on the larger climate system, highlighting issues in aerosol-cloud interaction modelling.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Short summary
Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
Short summary
Short summary
In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Yusuf A. Bhatti, Laura E. Revell, Alex J. Schuddeboom, Adrian J. McDonald, Alex T. Archibald, Jonny Williams, Abhijith U. Venugopal, Catherine Hardacre, and Erik Behrens
Atmos. Chem. Phys., 23, 15181–15196, https://doi.org/10.5194/acp-23-15181-2023, https://doi.org/10.5194/acp-23-15181-2023, 2023
Short summary
Short summary
Aerosols are a large source of uncertainty over the Southern Ocean. A dominant source of sulfate aerosol in this region is dimethyl sulfide (DMS), which is poorly simulated by climate models. We show the sensitivity of simulated atmospheric DMS to the choice of oceanic DMS data set and emission scheme. We show that oceanic DMS has twice the influence on atmospheric DMS than the emission scheme. Simulating DMS more accurately in climate models will help to constrain aerosol uncertainty.
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.
Aishwarya Raman, Thomas Hill, Paul J. DeMott, Balwinder Singh, Kai Zhang, Po-Lun Ma, Mingxuan Wu, Hailong Wang, Simon P. Alexander, and Susannah M. Burrows
Atmos. Chem. Phys., 23, 5735–5762, https://doi.org/10.5194/acp-23-5735-2023, https://doi.org/10.5194/acp-23-5735-2023, 2023
Short summary
Short summary
Ice-nucleating particles (INPs) play an important role in cloud processes and associated precipitation. Yet, INPs are not accurately represented in climate models. This study attempts to uncover these gaps by comparing model-simulated INP concentrations against field campaign measurements in the SO for an entire year, 2017–2018. Differences in INP concentrations and variability between the model and observations have major implications for modeling cloud properties in high latitudes.
Peter Kuma, Frida A.-M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland
Atmos. Chem. Phys., 23, 523–549, https://doi.org/10.5194/acp-23-523-2023, https://doi.org/10.5194/acp-23-523-2023, 2023
Short summary
Short summary
We present a machine learning method for determining cloud types in climate model output and satellite observations based on ground observations of cloud genera. We analyse cloud type biases and changes with temperature in climate models and show that the bias is anticorrelated with climate sensitivity. Models simulating decreasing stratiform and increasing cumuliform clouds with increased CO2 concentration tend to have higher climate sensitivity than models simulating the opposite tendencies.
Sonya L. Fiddes, Alain Protat, Marc D. Mallet, Simon P. Alexander, and Matthew T. Woodhouse
Atmos. Chem. Phys., 22, 14603–14630, https://doi.org/10.5194/acp-22-14603-2022, https://doi.org/10.5194/acp-22-14603-2022, 2022
Short summary
Short summary
Climate models have difficulty simulating Southern Ocean clouds, impacting how much sunlight reaches the surface. We use machine learning to group different cloud types observed from satellites and simulated in a climate model. We find the model does a poor job of simulating the same cloud type as what the satellite shows and, even when it does, the cloud properties and amount of reflected sunlight are incorrect. We have a lot of work to do to model clouds correctly over the Southern Ocean.
Adrien Guyot, Alain Protat, Simon P. Alexander, Andrew R. Klekociuk, Peter Kuma, and Adrian McDonald
Atmos. Meas. Tech., 15, 3663–3681, https://doi.org/10.5194/amt-15-3663-2022, https://doi.org/10.5194/amt-15-3663-2022, 2022
Short summary
Short summary
Ceilometers are instruments that are widely deployed as part of operational networks. They are usually not able to detect cloud phase. Here, we propose an evaluation of various methods to detect supercooled liquid water with ceilometer observations, using an extensive dataset from Davis, Antarctica. Our results highlight the possibility for ceilometers to detect supercooled liquid water in clouds.
Alex R. Aves, Laura E. Revell, Sally Gaw, Helena Ruffell, Alex Schuddeboom, Ngaire E. Wotherspoon, Michelle LaRue, and Adrian J. McDonald
The Cryosphere, 16, 2127–2145, https://doi.org/10.5194/tc-16-2127-2022, https://doi.org/10.5194/tc-16-2127-2022, 2022
Short summary
Short summary
This study confirms the presence of microplastics in Antarctic snow, highlighting the extent of plastic pollution globally. Fresh snow was collected from Ross Island, Antarctica, and subsequent analysis identified an average of 29 microplastic particles per litre of melted snow. The most likely source of these airborne microplastics is local scientific research stations; however, modelling shows their origin could have been up to 6000 km away.
Sonya L. Fiddes, Matthew T. Woodhouse, Steve Utembe, Robyn Schofield, Simon P. Alexander, Joel Alroe, Scott D. Chambers, Zhenyi Chen, Luke Cravigan, Erin Dunne, Ruhi S. Humphries, Graham Johnson, Melita D. Keywood, Todd P. Lane, Branka Miljevic, Yuko Omori, Alain Protat, Zoran Ristovski, Paul Selleck, Hilton B. Swan, Hiroshi Tanimoto, Jason P. Ward, and Alastair G. Williams
Atmos. Chem. Phys., 22, 2419–2445, https://doi.org/10.5194/acp-22-2419-2022, https://doi.org/10.5194/acp-22-2419-2022, 2022
Short summary
Short summary
Coral reefs have been found to produce the climatically relevant chemical compound dimethyl sulfide (DMS). It has been suggested that corals can modify their environment via the production of DMS. We use an atmospheric chemistry model to test this theory at a regional scale for the first time. We find that it is unlikely that coral-reef-derived DMS has an influence over local climate, in part due to the proximity to terrestrial and anthropogenic aerosol sources.
Paola Formenti, Claudia Di Biagio, Yue Huang, Jasper Kok, Marc Daniel Mallet, Damien Boulanger, and Mathieu Cazaunau
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-403, https://doi.org/10.5194/amt-2021-403, 2021
Publication in AMT not foreseen
Short summary
Short summary
This paper provides with standardized correction factors for the measurements of the most common instruments used in the atmosphere to measure the concentration per size of aerosol particles. These correction factors are provided to users with supplementary information for their use.
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.
Ethan R. Dale, Stefanie Kremser, Jordis S. Tradowsky, Greg E. Bodeker, Leroy J. Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, Jan-Niklas Schmidt, Nariefa Abrahim, Adrian J. McDonald, and Peter Kuma
Earth Syst. Sci. Data, 13, 2053–2075, https://doi.org/10.5194/essd-13-2053-2021, https://doi.org/10.5194/essd-13-2053-2021, 2021
Short summary
Short summary
MAPM is a project whose goal is to develop a method to infer particulate matter (PM) emissions maps from PM concentration measurements. In support of MAPM, we conducted a winter field campaign in New Zealand. In addition to two types of instruments measuring PM, an array of other meteorological sensors were deployed, measuring temperature and wind speed as well as probing the vertical structure of the lower atmosphere. In this article, we present the measurements taken during this campaign.
Sonya L. Fiddes, Matthew T. Woodhouse, Todd P. Lane, and Robyn Schofield
Atmos. Chem. Phys., 21, 5883–5903, https://doi.org/10.5194/acp-21-5883-2021, https://doi.org/10.5194/acp-21-5883-2021, 2021
Short summary
Short summary
Coral reefs are known to produce the aerosol precursor dimethyl sulfide (DMS). Currently, this source of coral DMS is unaccounted for in climate modelling, and the impact of coral reef extinction on aerosol and climate is unknown. In this study, we address this problem using a coupled chemistry–climate model for the first time. We find that coral reefs make a minimal contribution to the aerosol population and are unlikely to play a role in climate modulation.
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
Alexander, S. and McDonald, A.: University of Canterbury's Vaisala CL51 Ceilometer at Macquarie Island 2016-2018, Version 1, Australian Antarctic Data Centre [data set], https://doi.org/10.26179/5d91835e2ccc3, 2020. a
Allan, R. P.: Evaluation of simulated clear-sky longwave radiation using ground-based observations, J. Climate, 13, 1951–1964, https://doi.org/10.1175/1520-0442(2000)013<1951:EOSCSL>2.0.CO;2, 2000. a, b
Bennartz, R., Shupe, M. D., Turner, D. D., Walden, V. P., Steffen, K., Cox, C. J., Kulie, M. S., Miller, N. B., and Pettersen, C.: July 2012 Greenland melt extent enhanced by low-level liquid clouds, Nature, 496, 83–86, https://doi.org/10.1038/nature12002, 2013. a
Bi, D., Dix, M., Marsland, S., O'farrell, S., Sullivan, A., Bodman, R., Law, R., Harman, I., Srbinovsky, J., Rashid, H. A., Dobrohotoff, P., Mackallah, C., Yan, H., Hirst, A., Savita, A., Dias, F. B., Woodhouse, M., Fiedler, R., and Heerdegen, A.: Configuration and spin-up of ACCESS-CM2, the new generation Australian Community Climate and Earth System Simulator Coupled Model, Journal of Southern Hemisphere Earth Systems Science, 70, 225–251, https://doi.org/10.1071/ES19040, 2020. a, b
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J. L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011. a
Bodas-Salcedo, A., Williams, K. D., Field, P. R., and Lock, A. P.: The surface downwelling solar radiation surplus over the southern ocean in the met office model: The role of midlatitude cyclone clouds, J. Climate, 25, 7467–7486, https://doi.org/10.1175/JCLI-D-11-00702.1, 2012. a, b, c
Bodas-Salcedo, A., Williams, K. D., Ringer, M. A., Beau, I., Cole, J. N., Dufresne, J. L., Koshiro, T., Stevens, B., Wang, Z., and Yokohata, T.: Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models, J. Climate, 27, 41–56, https://doi.org/10.1175/JCLI-D-13-00169.1, 2014. a, b, c, d
Bodas-Salcedo, A., Hill, P. G., Furtado, K., Williams, K. D., Field, P. R., Manners, J. C., Hyder, P., and Kato, S.: Large contribution of supercooled liquid clouds to the solar radiation budget of the Southern Ocean, J. Climate, 29, 4213–4228, https://doi.org/10.1175/JCLI-D-15-0564.1, 2016. a, b
Bodman, R. W., Karoly, D. J., Dix, M. R., Harman, I. N., Srbinovsky, J., Dobrohotoff, P. B., and Mackallah, C.: Evaluation of CMIP6 AMIP climate simulations with the ACCESS-AM2 model, Journal of Southern Hemisphere Earth Systems Science, 70, 166–179, https://doi.org/10.1071/ES19033, 2020. a, b
Bohren, C. F. and Huffman, D. R.: Absorption and scattering of light by small particles, John Wiley & Sons, https://doi.org/10.1002/9783527618156, 2008. a
Campbell, J. R., Hlavka, D. L., Welton, E. J., Flynn, C. J., Turner, D. D., Spinhirne, J. D., Scott III, V. S., and Hwang, I.: Full-time, eye-safe cloud and aerosol lidar observation at atmospheric radiation measurement program sites: Instruments and data processing, J. Atmos. Ocean. Tech., 19, 431–442, 2002. a
Ceppi, P., Hwang, Y. T., Frierson, D. M., and Hartmann, D. L.: Southern Hemisphere jet latitude biases in CMIP5 models linked to shortwave cloud forcing, Geophys. Res. Lett., 39, 1–5, https://doi.org/10.1029/2012GL053115, 2012. a
Cesana, G. and Storelvmo, T.: Improving climate projections by understanding how cloud phase affects radiation, J. Geophys. Res.-Atmos., 122, 4594–4599, 2017. a
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
Chepfer, H., Bony, S., Winker, D., Chiriaco, M., Dufresne, J.-L., and Sèze, G.: Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model, Geophys. Res. Lett., 35, L15704, https://doi.org/10.1029/2008GL034207, 2008. a
Corripio, J. G.: Vectorial algebra algorithms for calculating terrain parameters from dems and solar radiation modelling in mountainous terrain, Int. J. Geogr. Inf. Sci., 17, 1–23, https://doi.org/10.1080/713811744, 2003. a
Dommenget, D. and Flöter, J.: Conceptual understanding of climate change with a globally resolved energy balance model, Clim. Dynam., 37, 2143–2165, 2011. a
Edwards, J. M. and Slingo, A.: Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model, Q. J. Roy. Meteor. Soc., 122, 689–719, https://doi.org/10.1256/smsqj.53106, 1996. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a, b
Fairall, C., Uttal, T., Hazen, D., Hare, J., Cronin, M. F., Bond, N., and Veron, D. E.: Observations of cloud, radiation, and surface forcing in the equatorial eastern Pacific, J. Climate, 21, 655–673, 2008. a
Fiddes, S. L., Woodhouse, M. T., Nicholls, Z., Lane, T. P., and Schofield, R.: Cloud, precipitation and radiation responses to large perturbations in global dimethyl sulfide, Atmos. Chem. Phys., 18, 10177–10198, https://doi.org/10.5194/acp-18-10177-2018, 2018. a
Fiddes, S. L., Protat, A., Mallet, M. D., Alexander, S. P., and Woodhouse, M. T.: Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right?, Atmos. Chem. Phys., 22, 14603–14630, https://doi.org/10.5194/acp-22-14603-2022, 2022. a, b, c, d, e, f, g, h, i
Field, P. R., Bodas-Salcedo, A., and Brooks, M. E.: Using model analysis and satellite data to assess cloud and precipitation in midlatitude cyclones, Q. J. Roy. Meteor. Soc., 137, 1501–1515, https://doi.org/10.1002/qj.858, 2011. a
Franklin, C. N., Sun, Z., Bi, D., Dix, M., Yan, H., and Bodas-Salcedo, A.: Evaluation of clouds in access using the satellite simulator package cosp: Global, seasonal, and regional cloud properties, J. Geophys. Res.-Atmos., 118, 732–748, https://doi.org/10.1029/2012JD018469, 2013. a, b, c
French, J., Pei, Z., Alexander, S., Fiddes, S., Mallet, M., Kuma, P., and McDonald, A.: Macquarie_Island_Radiometer_and_AllSkyCam_Data_2016-2018, Ver. 1, Australian Antarctic Data Centre [data set], https://doi.org/10.26179/6seh-er53, 2023. a
Furtado, K. and Field, P.: The role of ice microphysics parametrizations in determining the prevalence of supercooled liquid water in high-resolution simulations of a Southern Ocean midlatitude cyclone, J. Atmos. Sci., 74, 2001–2021, 2017. a
Gettelman, A., Bardeen, C. G., McCluskey, C. S., Järvinen, E., Stith, J., Bretherton, C., McFarquhar, G., Twohy, C., D'Alessandro, J., and Wu, W.: Simulating Observations of Southern Ocean Clouds and Implications for Climate, J. Geophys. Res.-Atmos., 125, 1–22, https://doi.org/10.1029/2020JD032619, 2020. a, b, c, d
Ghonima, M. S., Urquhart, B., Chow, C. W., Shields, J. E., Cazorla, A., and Kleissl, J.: A method for cloud detection and opacity classification based on ground based sky imagery, Atmos. Meas. Tech., 5, 2881–2892, https://doi.org/10.5194/amt-5-2881-2012, 2012. a
Guyot, A., Protat, A., Alexander, S. P., Klekociuk, A. R., Kuma, P., and McDonald, A.: Detection of supercooled liquid water clouds with ceilometers: Development and evaluation of deterministic and data-driven retrievals, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-10, in review, 2022. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
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 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Hu, Y., Rodier, S., Xu, K. M., Sun, W., Huang, J., Lin, B., Zhai, P., and Josset, D.: Occurrence, liquid water content, and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements, J. Geophys. Res.-Atmos., 115, 1–13, https://doi.org/10.1029/2009JD012384, 2010. a
Hurrell, J. W., Hack, J. J., Shea, D., Caron, J. M., and Rosinski, J.: A new sea surface temperature and sea ice boundary dataset for the community atmosphere model, J. Climate, 21, 5145–5153, https://doi.org/10.1175/2008JCLI2292.1, 2008. a
Hwang, Y. T. and Frierson, D. M.: Link between the double-intertropical convergence zone problem and cloud biases over the southern ocean, P. Natl. Acad. Sci. USA, 110, 4935–4940, https://doi.org/10.1073/pnas.1213302110, 2013. a
Hyder, P., Edwards, J. M., Allan, R. P., Hewitt, H. T., Bracegirdle, T. J., Gregory, J. M., Wood, R. A., Meijers, A. J., Mulcahy, J., Field, P., Furtado, K., Bodas-Salcedo, A., Williams, K. D., Copsey, D., Josey, S. A., Liu, C., Roberts, C. D., Sanchez, C., Ridley, J., Thorpe, L., Hardiman, S. C., Mayer, M., Berry, D. I., and Belcher, S. E.: Critical Southern Ocean climate model biases traced to atmospheric model cloud errors, Nat. Commun., 9, 3625, https://doi.org/10.1038/s41467-018-05634-2, 2018. a
Idso, S. B.: A set of equations for full spectrum and 8- to 14-µm and 10.5- to 12.5-µm thermal radiation from cloudless skies, Water Resour. Res., 17, 295–304, 1981. a
Illingworth, A., Hogan, R., O'connor, E., Bouniol, D., Brooks, M., Delanoë, J., Donovan, D., Eastment, J., Gaussiat, N., Goddard, J., Haeffelin, M., Baltink, H. K., Krasnov, O. A., Pelon, J., Piriou, J.-M., Protat, A., Russchenberg, H. W. J., Seifert, A., Tompkins, A. M., van Zadelhoff, G.-J., Vinit, F., Willén, U., Wilson, D. R., and Wrench, C. L.: Cloudnet: Continuous evaluation of cloud profiles in seven operational models using ground-based observations, B. Am. Meteorol. Soc., 88, 883–898, 2007. a
Illingworth, A. J., Cimini, D., Haefele, A., Haeffelin, M., Hervo, M., Kotthaus, S., Löhnert, U., Martinet, P., Mattis, I., O’Connor, E. J., and Potthast, R.: How can existing ground-based profiling instruments improve European weather forecasts?, B. Am. Meteorol. Soc., 100, 605–619, 2019. a
Kay, J. E., Wall, C., Yettella, V., Medeiros, B., Hannay, C., Caldwell, P., and Bitz, C.: No access global climate impacts of fixing the Southern Ocean shortwave radiation bias in the Community Earth System Model (CESM), J. Climate, 29, 4617–4636, https://doi.org/10.1175/JCLI-D-15-0358.1, 2016. a
Kotthaus, S., O'Connor, E., Münkel, C., Charlton-Perez, C., Haeffelin, M., Gabey, A. M., and Grimmond, C. S. B.: Recommendations for processing atmospheric attenuated backscatter profiles from Vaisala CL31 ceilometers, Atmos. Meas. Tech., 9, 3769–3791, https://doi.org/10.5194/amt-9-3769-2016, 2016. a
Kremser, S., Harvey, M., Kuma, P., Hartery, S., Saint-Macary, A., McGregor, J., Schuddeboom, A., von Hobe, M., Lennartz, S. T., Geddes, A., Querel, R., McDonald, A., Peltola, M., Sellegri, K., Silber, I., Law, C. S., Flynn, C. J., Marriner, A., Hill, T. C. J., DeMott, P. J., Hume, C. C., Plank, G., Graham, G., and Parsons, S.: Southern Ocean cloud and aerosol data: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage, Earth Syst. Sci. Data, 13, 3115–3153, https://doi.org/10.5194/essd-13-3115-2021, 2021. a
Kuma, P., McDonald, A. J., Morgenstern, O., Alexander, S. P., Cassano, J. J., Garrett, S., Halla, J., Hartery, S., Harvey, M. J., Parsons, S., Plank, G., Varma, V., and Williams, J.: Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations, Atmos. Chem. Phys., 20, 6607–6630, https://doi.org/10.5194/acp-20-6607-2020, 2020. a, b, c, d, e
Kuma, P., McDonald, A. J., Morgenstern, O., Querel, R., Silber, I., and Flynn, C. J.: Automatic Lidar and Ceilometer Framework (ALCF), Version 1.0.0, Zenodo [software], https://doi.org/10.5281/zenodo.4411633, 2021a. a
Kuma, P., Bender, F. A.-M., and Jönsson, A. R.: Climate model code genealogy and its relation to climate feedbacks and sensitivity, J. Adv. Model. Earth Sy., 15, e2022MS003588, https://doi.org/10.1029/2022MS003588, 2023. a
Lawson, R. P. and Gettelman, A.: Impact of Antarctic mixed-phase clouds on climate, P. Natl. Acad. Sci. USA, 111, 18156–18161, https://doi.org/10.1073/pnas.1418197111, 2014. a
Li, Q., Lu, W., and Yang, J.: A hybrid thresholding algorithm for cloud detection on ground-based color images, J. Atmos. Ocean. Tech., 28, 1286–1296, https://doi.org/10.1175/JTECH-D-11-00009.1, 2011. a
Mallet, M. D., Alexander, S. P., Protat, A., and Fiddes, S. L.: Reducing Southern Ocean shortwave radiation errors in the ERA5 reanalysis with machine learning and 25 years of surface observations, Artificial Intelligence for the Earth Systems, 2, e220044, https://doi.org/10.1175/AIES-D-22-0044.1, 2023. a, b
McCoy, D. T., Hartmann, D. L., Zelinka, M. D., Ceppi, P., and Grosvenor, D. P.: Mixed-phase cloud physics and Southern Ocean cloud feedback in climate models, J. Geophys. Res.-Atmos., 120, 9539–9554, 2015. a
McErlich, C., McDonald, A., Schuddeboom, A., and Silber, I.: Comparing Satellite-and Ground-Based Observations of Cloud Occurrence Over High Southern Latitudes, J. Geophys. Res.-Atmos., 126, e2020JD033607, https://doi.org/10.1029/2020JD033607, 2021. a, b
Nam, C., Bony, S., Dufresne, J.-L., and Chepfer, H.: The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39, L21801, https://doi.org/10.1029/2012GL053421, 2012. a
NASA: CERES Data Products, NASA [data set], https://ceres.larc.nasa.gov/data/, last access: 21 January 2023. a
O'Connor, E. J., Illingworth, A. J., and Hogan, R. J.: A technique for autocalibration of cloud lidar, J. Atmos. Ocean. Tech., 21, 777–786, https://doi.org/10.1175/1520-0426(2004)021<0777:ATFAOC>2.0.CO;2, 2004. a
Pei, Z. and Fiddes, S. L.: ACCESS-AM2 daily and hourly cloud and radiation data for model evaluation at Macquarie Island, Zenodo [data set], https://doi.org/10.5281/zenodo.10082306, 2023. a
Ruiz-Arias, J. A., Dudhia, J., Santos-Alamillos, F. J., and Pozo-Vázquez, D.: Surface clear-sky shortwave radiative closure intercomparisons in the Weather Research and Forecasting model, J. Geophys. Res.-Atmos., 118, 9901–9913, https://doi.org/10.1002/jgrd.50778, 2013. a
Rutan, D. A., Kato, S., Doelling, D. R., Rose, F. G., Nguyen, L. T., Caldwell, T. E., and Loeb, N. G.: CERES synoptic product: Methodology and validation of surface radiant flux, J. Atmos. Ocean. Tech., 32, 1121–1143, https://doi.org/10.1175/JTECH-D-14-00165.1, 2015. a
Schuddeboom, A. J. and McDonald, A. J.: The Southern Ocean Radiative Bias, Cloud Compensating Errors, and Equilibrium Climate Sensitivity in CMIP6 Models, Journal of Geophysical Research: Atmospheres, 126, e2021JD035310, https://doi.org/10.1029/2021JD035310, 2021. a, b, c
Sengupta, M., Habte, A., Andreas, A., Reda, I., Jaker, S., Xie, Y., Yang, J., Gotseff, P., Kutchenreiter, M., and Shi, Y.: Sky Radiometers on Stand for Downwelling Radiation (SKYRAD60S), Atmospheric Radiation Measurement (ARM) User Facility [data set], https://doi.org/10.5439/1377836, 2016. a
Shakespeare, C. J. and Roderick, M. L.: The clear-sky downwelling long-wave radiation at the surface in current and future climates, Q. J. Roy. Meteor. Soc., 147, 4251–4268, 2021. 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
Stanford, M. W., Fridlind, A. M., Silber, I., Ackerman, A. S., Cesana, G., Mülmenstädt, J., Protat, A., Alexander, S., and McDonald, A.: Earth-system-model evaluation of cloud and precipitation occurrence for supercooled and warm clouds over the Southern Ocean's Macquarie Island, Atmos. Chem. Phys., 23, 9037–9069, https://doi.org/10.5194/acp-23-9037-2023, 2023. a
Tan, I. and Storelvmo, T.: Sensitivity study on the influence of cloud microphysical parameters on mixed-phase cloud thermodynamic phase partitioning in CAM5, J. Atmos. Sci., 73, 709–728, 2016. a
Tansey, E., Marchand, R., Protat, A., Alexander, S. P., and Ding, S.: Southern Ocean precipitation characteristics observed from CloudSat and ground instrumentation during the Macquarie Island Cloud & Radiation Experiment (MICRE): April 2016 to March 2017, J. Geophys. Res.-Atmos., 127, e2021JD035370, https://doi.org/10.1029/2021JD035370, 2022. a, b, c
Trenberth, K. E. and Fasullo, J. T.: Simulation of present-day and twenty-first-century energy budgets of the southern oceans, J. Climate, 23, 440–454, https://doi.org/10.1175/2009JCLI3152.1, 2010. a, b, c, d
Trenberth, K. E., Fasullo, J. T., and Kiehl, J.: Earth's global energy budget, B. Am. Meteorol. Soc., 90, 311–323, https://doi.org/10.1175/2008BAMS2634.1, 2009. a
Vergara-Temprado, J., Miltenberger, A. K., Furtado, K., Grosvenor, D. P., Shipway, B. J., Hill, A. A., Wilkinson, J. M., Field, P. R., Murray, B. J., and Carslaw, K. S.: Strong control of Southern Ocean cloud reflectivity by ice-nucleating particles, P. Natl. Acad. Sci. USA, 115, 2687–2692, 2018. a, b
Viúdez-Mora, A., Costa-Surós, M., Calbó, J., and González, J.: Modeling atmospheric longwave radiation at the surface during overcast skies: The role of cloud base height, J. Geophys. Res.-Atmos., 120, 199–214, 2015. a
Wall, C. J., Hartmann, D. L., and Ma, P.-L.: Instantaneous linkages between clouds and large-scale meteorology over the Southern Ocean in observations and a climate model, J. Climate, 30, 9455–9474, 2017. a
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019. a
Wang, Z., Siems, S. T., Belusic, D., Manton, M. J., and Huang, Y.: A climatology of the precipitation over the Southern Ocean as observed at Macquarie Island, J. Appl. Meteorol. Clim., 54, 2321–2337, https://doi.org/10.1175/JAMC-D-14-0211.1, 2015. a
Wild, M., Ohmura, A., Gilgen, H., Morcrette, J.-J., and Slingo, A.: Evaluation of downward longwave radiation in general circulation models, J. Climate, 14, 3227–3239, 2001. a
Wild, M., Long, C. N., and Ohmura, A.: Evaluation of clear-sky solar fluxes in GCMs participating in AMIP and IPCC-AR4 from a surface perspective, J. Geophys. Res.-Atmos., 111, 1–15, https://doi.org/10.1029/2005JD006118, 2006. a
Williams, K. D. and Webb, M. J.: A quantitative performance assessment of cloud regimes in climate models, Clim. Dynam., 33, 141–157, https://doi.org/10.1007/s00382-008-0443-1, 2009. a
Yabuki, M., Shiobara, M., Nishinaka, K., and Kuji, M.: Development of a cloud detection method from whole-sky color images, Polar Sci., 8, 315–326, 2014. 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, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a
Zhang, X., Liang, S., Wang, G., Yao, Y., Jiang, B., and Cheng, J.: Evaluation of the reanalysis surface incident shortwave radiation products from NCEP, ECMWF, GSFC, and JMA using satellite and surface observations, Remote Sens.-Basel, 8, 225, https://doi.org/10.3390/rs8030225, 2016. a
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(7431 KB) - Full-text XML
Short summary
In this paper, we use ground-based observations to evaluate a climate model and a satellite product in simulating surface radiation and investigate how radiation biases are influenced by cloud properties over the Southern Ocean. We find that significant radiation biases exist in both the model and satellite. The cloud fraction and cloud occurrence play an important role in affecting radiation biases. We suggest further development for the model and satellite using ground-based observations.
In this paper, we use ground-based observations to evaluate a climate model and a satellite...
Altmetrics
Final-revised paper
Preprint