Articles | Volume 22, issue 22
https://doi.org/10.5194/acp-22-14603-2022
© 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-22-14603-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right?
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Climate Science Centre, Oceans and Atmosphere, Commonwealth Scientific and Industrial Research Organisation, Aspendale, Australia
Alain Protat
Bureau of Meteorology, Melbourne, 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
Simon P. Alexander
Australian Antarctic Division, Hobart, Australia
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Matthew T. Woodhouse
Climate Science Centre, Oceans and Atmosphere, Commonwealth Scientific and Industrial Research Organisation, Aspendale, Australia
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
Related authors
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.
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.
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.
Zhangcheng Pei, Sonya L. Fiddes, W. John R. French, Simon P. Alexander, Marc D. Mallet, Peter Kuma, and Adrian McDonald
Atmos. Chem. Phys., 23, 14691–14714, https://doi.org/10.5194/acp-23-14691-2023, https://doi.org/10.5194/acp-23-14691-2023, 2023
Short summary
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.
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.
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.
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.
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.
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.
Luis Ackermann, Joshua Soderholm, Alain Protat, Rhys Whitley, Lisa Ye, and Nina Ridder
Atmos. Meas. Tech., 17, 407–422, https://doi.org/10.5194/amt-17-407-2024, https://doi.org/10.5194/amt-17-407-2024, 2024
Short summary
Short summary
The paper addresses the crucial topic of hail damage quantification using radar observations. We propose a new radar-derived hail product that utilizes a large dataset of insurance hail damage claims and radar observations. A deep neural network was employed, trained with local meteorological variables and the radar observations, to better quantify hail damage. Key meteorological variables were identified to have the most predictive capability in this regard.
Ben A. Cala, Scott Archer-Nicholls, James Weber, N. Luke Abraham, Paul T. Griffiths, Lorrie Jacob, Y. Matthew Shin, Laura E. Revell, Matthew Woodhouse, and Alexander T. Archibald
Atmos. Chem. Phys., 23, 14735–14760, https://doi.org/10.5194/acp-23-14735-2023, https://doi.org/10.5194/acp-23-14735-2023, 2023
Short summary
Short summary
Dimethyl sulfide (DMS) is an important trace gas emitted from the ocean recognised as setting the sulfate aerosol background, but its oxidation is complex. As a result representation in chemistry-climate models is greatly simplified. We develop and compare a new mechanism to existing mechanisms via a series of global and box model experiments. Our studies show our updated DMS scheme is a significant improvement but significant variance exists between mechanisms.
Zhangcheng Pei, Sonya L. Fiddes, W. John R. French, Simon P. Alexander, Marc D. Mallet, Peter Kuma, and Adrian McDonald
Atmos. Chem. Phys., 23, 14691–14714, https://doi.org/10.5194/acp-23-14691-2023, https://doi.org/10.5194/acp-23-14691-2023, 2023
Short summary
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.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
Short summary
Short summary
We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
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.
Ruhi S. Humphries, Melita D. Keywood, Jason P. Ward, James Harnwell, Simon P. Alexander, Andrew R. Klekociuk, Keiichiro Hara, Ian M. McRobert, Alain Protat, Joel Alroe, Luke T. Cravigan, Branka Miljevic, Zoran D. Ristovski, Robyn Schofield, Stephen R. Wilson, Connor J. Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Greg M. McFarquhar, Scott D. Chambers, Alastair G. Williams, and Alan D. Griffiths
Atmos. Chem. Phys., 23, 3749–3777, https://doi.org/10.5194/acp-23-3749-2023, https://doi.org/10.5194/acp-23-3749-2023, 2023
Short summary
Short summary
Observations of aerosols in pristine regions are rare but are vital to constraining the natural baseline from which climate simulations are calculated. Here we present recent seasonal observations of aerosols from the Southern Ocean and contrast them with measurements from Antarctica, Australia and regionally relevant voyages. Strong seasonal cycles persist, but striking differences occur at different latitudes. This study highlights the need for more long-term observations in remote regions.
Ashok K. Luhar, Ian E. Galbally, and Matthew T. Woodhouse
Atmos. Chem. Phys., 22, 13013–13033, https://doi.org/10.5194/acp-22-13013-2022, https://doi.org/10.5194/acp-22-13013-2022, 2022
Short summary
Short summary
Recent improvements to global parameterisations of oceanic ozone dry deposition and lightning-generated oxides of nitrogen (LNOx) have consequent impacts on earth's radiative fluxes. Uncertainty in radiative fluxes arising from uncertainty in LNOx is of significant magnitude in comparison with the
present-dayIPCC AR6 anthropogenic effective radiative forcing (ERF) due to ozone. Hence, uncertainty in LNOx needs to be explicitly addressed in relation to the GWP and ERF of anthropogenic methane.
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.
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.
Alain Protat, Valentin Louf, Joshua Soderholm, Jordan Brook, and William Ponsonby
Atmos. Meas. Tech., 15, 915–926, https://doi.org/10.5194/amt-15-915-2022, https://doi.org/10.5194/amt-15-915-2022, 2022
Short summary
Short summary
This study uses collocated ship-based, ground-based, and spaceborne radar observations to validate the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks to the accuracy required for operational severe weather applications such as rainfall and hail nowcasting.
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.
Kamil Mroz, Alessandro Battaglia, Cuong Nguyen, Andrew Heymsfield, Alain Protat, and Mengistu Wolde
Atmos. Meas. Tech., 14, 7243–7254, https://doi.org/10.5194/amt-14-7243-2021, https://doi.org/10.5194/amt-14-7243-2021, 2021
Short summary
Short summary
A method for estimating microphysical properties of ice clouds based on radar measurements is presented. The algorithm exploits the information provided by differences in the radar response at different frequency bands in relation to changes in the snow morphology. The inversion scheme is based on a statistical relation between the radar simulations and the properties of snow calculated from in-cloud sampling.
Ruhi S. Humphries, Melita D. Keywood, Sean Gribben, Ian M. McRobert, Jason P. Ward, Paul Selleck, Sally Taylor, James Harnwell, Connor Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Alain Protat, Simon P. Alexander, and Greg McFarquhar
Atmos. Chem. Phys., 21, 12757–12782, https://doi.org/10.5194/acp-21-12757-2021, https://doi.org/10.5194/acp-21-12757-2021, 2021
Short summary
Short summary
The Southern Ocean region is one of the most pristine in the world and serves as an important proxy for the pre-industrial atmosphere. Improving our understanding of the natural processes in this region is likely to result in the largest reductions in the uncertainty of climate and earth system models. In this paper we present a statistical summary of the latitudinal gradient of aerosol and cloud condensation nuclei concentrations obtained from five voyages spanning the Southern Ocean.
Ashok K. Luhar, Ian E. Galbally, Matthew T. Woodhouse, and Nathan Luke Abraham
Atmos. Chem. Phys., 21, 7053–7082, https://doi.org/10.5194/acp-21-7053-2021, https://doi.org/10.5194/acp-21-7053-2021, 2021
Short summary
Short summary
Lightning-generated nitrogen oxides (LNOx) greatly influence tropospheric photochemistry. The most common parameterisation of lightning flash rate used to calculate LNOx in global composition models underestimates measurements over the ocean by a factor of 20–25. We formulate and validate an alternative parameterisation to remedy this problem. The new scheme causes an increase in the ozone burden by 8.5 % and the hydroxyl radical by 13 %, and these have implications for climate and air quality.
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.
Robert Jackson, Scott Collis, Valentin Louf, Alain Protat, Die Wang, Scott Giangrande, Elizabeth J. Thompson, Brenda Dolan, and Scott W. Powell
Atmos. Meas. Tech., 14, 53–69, https://doi.org/10.5194/amt-14-53-2021, https://doi.org/10.5194/amt-14-53-2021, 2021
Short summary
Short summary
About 4 years of 2D video disdrometer data in Darwin are used to develop and validate rainfall retrievals for tropical convection in C- and X-band radars in Darwin. Using blended techniques previously used for Colorado and Manus and Gan islands, with modified coefficients in each estimator, provided the most optimal results. Using multiple radar observables to develop a rainfall retrieval provided a greater advantage than using a single observable, including using specific attenuation.
Jane P. Mulcahy, Colin Johnson, Colin G. Jones, Adam C. Povey, Catherine E. Scott, Alistair Sellar, Steven T. Turnock, Matthew T. Woodhouse, Nathan Luke Abraham, Martin B. Andrews, Nicolas Bellouin, Jo Browse, Ken S. Carslaw, Mohit Dalvi, Gerd A. Folberth, Matthew Glover, Daniel P. Grosvenor, Catherine Hardacre, Richard Hill, Ben Johnson, Andy Jones, Zak Kipling, Graham Mann, James Mollard, Fiona M. O'Connor, Julien Palmiéri, Carly Reddington, Steven T. Rumbold, Mark Richardson, Nick A. J. Schutgens, Philip Stier, Marc Stringer, Yongming Tang, Jeremy Walton, Stephanie Woodward, and Andrew Yool
Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, https://doi.org/10.5194/gmd-13-6383-2020, 2020
Short summary
Short summary
Aerosols are an important component of the Earth system. Here, we comprehensively document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 and UKESM1. This study provides a useful characterisation of the aerosol climatology in both models, facilitating the understanding of the numerous aerosol–climate interaction studies that will be conducted for CMIP6 and beyond.
Cited articles
Anderberg, M. R.: Cluster Analysis for Applications. A volume in Probability
and Mathematical Statistics: A Series of Monographs and Textbooks, vol. 19,
Academic Press, New York, https://doi.org/10.1016/C2013-0-06161-0, 1973. a
Bender, F. A., Engström, A., Wood, R., and Charlson, R. J.: Evaluation
of hemispheric asymmetries in marine cloud radiative properties, J.
Climate, 30, 4131–4147, https://doi.org/10.1175/JCLI-D-16-0263.1, 2017. a, b
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, c
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, b
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
Bodas-Salcedo, A., Williams, K. D., Ringer, M. A., Beau, I., Cole, J. N. S.,
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
Bodas-Salcedo, A., Andrews, T., Karmalkar, A. V., and Ringer, M. A.: Cloud
liquid water path and radiative feedbacks over the Southern Ocean,
Geophys. Res. Lett., 43, 938–10, https://doi.org/10.1002/2016GL070770,
2016a. a
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,
2016b. 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
Bony, S. and Dufresne, J. L.: Marine boundary layer clouds at the heart of
tropical cloud feedback uncertainties in climate models, Geophys.
Res. Lett., 32, L20806, https://doi.org/10.1029/2005GL023851, 2005. a
Calinski, T. and Harabasz, J.: A dendrite method for cluster analysis,
Communications in Statistics, 3, 1–27,
https://doi.org/10.1080/03610927408827101, 1974. a
Cho, N., Tan, J., and Oreopoulos, L.: Classifying planetary cloudiness with an
updated set of modis cloud regimes, J. Appl. Meteorol.
Clim., 60, 981–997, https://doi.org/10.1175/JAMC-D-20-0247.1, 2021. a
Chubb, T. H., Jensen, J. B., Siems, S. T., and Manton, M. J.: In situ
observations of supercooled liquid clouds over the Southern Ocean during the
HIAPER Pole-to-Pole Observation campaigns, Geophys. Res. Lett., 40,
5280–5285, https://doi.org/10.1002/grl.50986, 2013. a
Davies, D. L. and Bouldin, D. W.: A Cluster Separation Measure, IEEE
T. Pattern Anal., PAMI-1, 224–227,
https://doi.org/10.1109/TPAMI.1979.4766909, 1979. a
Doelling, D. R., Loeb, N. G., Keyes, D. F., Nordeen, M. L., Morstad, D.,
Nguyen, C., Wielicki, B. A., Young, D. F., and Sun, M.: Geostationary
enhanced temporal interpolation for ceres flux products, J.
Atmos. Ocean. Tech., 30, 1072–1090,
https://doi.org/10.1175/JTECH-D-12-00136.1, 2013. a
Doelling, D. R., Sun, M., Nguyen, L. T., Nordeen, M. L., Haney, C. O., Keyes,
D. F., and Mlynczak, P. E.: Advances in geostationary-derived longwave
fluxes for the CERES synoptic (SYN1deg) product, J. Atmos.
Ocean. Tech., 33, 503–521, https://doi.org/10.1175/JTECH-D-15-0147.1, 2016. 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/10.1002/qj.49712253107,
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
Feng, L., Smith, S. J., Braun, C., Crippa, M., Gidden, M. J., Hoesly, R., Klimont, Z., van Marle, M., van den Berg, M., and van der Werf, G. R.: The generation of gridded emissions data for CMIP6, Geosci. Model Dev., 13, 461–482, https://doi.org/10.5194/gmd-13-461-2020, 2020. a
Fiddes, S. L.: ACCESS-AM2 Southern Ocean cloud and radiation data for k-means clustering and analysis, Zenodo [data set], https://doi.org/10.5281/zenodo.6004062, 2022. 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, b
Field, P. R. and Wood, R.: Precipitation and cloud structure in midlatitude
cyclones, J. Climate, 20, 233–254, https://doi.org/10.1175/JCLI3998.1, 2007. a, b, c
Frey, W. R., Maroon, E. A., Pendergrass, A. G., and Kay, J. E.: Do Southern
Ocean Cloud Feedbacks Matter for 21st Century Warming?, Geophys. Res.
Lett., 44, 447–12, https://doi.org/10.1002/2017GL076339, 2017. a
Fricko, O., Havlik, P., Rogelj, J., Klimont, Z., Gusti, M., Johnson, N., Kolp,
P., Strubegger, M., Valin, H., Amann, M., Ermolieva, T., Forsell, N.,
Herrero, M., Heyes, C., Kindermann, G., Krey, V., McCollum, D. L.,
Obersteiner, M., Pachauri, S., Rao, S., Schmid, E., Schoepp, W., and Riahi,
K.: The marker quantification of the Shared Socioeconomic Pathway 2: A
middle-of-the-road scenario for the 21st century, Global Environmental
Change, 42, 251–267, https://doi.org/10.1016/j.gloenvcha.2016.06.004, 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, e2020JD032619,
https://doi.org/10.1029/2020JD032619, 2020. a, b, c, d
Gong, S. L.: A parameterization of sea-salt aerosol source function for sub-
and super-micron particles, Global Biogeochem. Cy., 17, 1097,
https://doi.org/10.1029/2003GB002079, 2003. a
Gregory, D. and Rowntree, P. R.: A Mass Flux Convection Scheme with
Representation of Cloud Ensemble Characteristics and Stability-Dependent
Closure, Mon. Weather Rev., 118, 1483–1506,
https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2, 1990. a
Hawcroft, M., Haywood, J. M., Collins, M., Jones, A., Jones, A. C., and
Stephens, G.: Southern Ocean albedo, inter-hemispheric energy transports and
the double ITCZ: global impacts of biases in a coupled model, Clim.
Dynam., 48, 2279–2295, https://doi.org/10.1007/s00382-016-3205-5, 2017. a, b
Haynes, J. M., Jakob, C., Rossow, W. B., Tselioudis, G., and Brown, J. B.:
Major characteristics of Southern Ocean cloud regimes and their effects on
the energy budget, J. Climate, 24, 5061–5080,
https://doi.org/10.1175/2011JCLI4052.1, 2011. a, b
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., 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., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hinkelman, L. M. and Marchand, R.: Evaluation of CERES and CloudSat Surface
Radiative Fluxes Over Macquarie Island, the Southern Ocean, Earth and Space
Science, 7, e2020EA001224, https://doi.org/10.1029/2020EA001224, 2020. a
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018. a
Holz, R. E., Platnick, S., Meyer, K., Vaughan, M., Heidinger, A., Yang, P., Wind, G., Dutcher, S., Ackerman, S., Amarasinghe, N., Nagle, F., and Wang, C.: Resolving ice cloud optical thickness biases between CALIOP and MODIS using infrared retrievals, Atmos. Chem. Phys., 16, 5075–5090, https://doi.org/10.5194/acp-16-5075-2016, 2016. a
Huang, Y., Siems, S. T., Manton, M. J., Protat, A., and Delanoë, J.: A
study on the low-altitude clouds over the Southern Ocean using the
DARDAR-MASK, J. Geophys. Res.-Atmos., 117, D18204,
https://doi.org/10.1029/2012JD017800, 2012. a
Hubanks, P., Pincus, R., Platnick, S., and Meyer, K.: Level-3 COSP Cloud
Properties (MCD06COSP_L3) Combined Terra & Aqua MODIS Global Gridded
Product for Climate Studies User Guide, Tech. Rep., NASA,
https://atmosphere-imager.gsfc.nasa.gov/sites/default/files/ModAtmo/documents/L3_MCD06COSP_UserGuide_v13.pdf (last access: 20 July 2021),
2020. a, b
Humphries, R. S., Keywood, M. D., Gribben, S., McRobert, I. M., Ward, J. P., Selleck, P., Taylor, S., Harnwell, J., Flynn, C., Kulkarni, G. R., Mace, G. G., Protat, A., Alexander, S. P., and McFarquhar, G.: Southern Ocean latitudinal gradients of cloud condensation nuclei, Atmos. Chem. Phys., 21, 12757–12782, https://doi.org/10.5194/acp-21-12757-2021, 2021. 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, b
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
Jakob, C. and Tselioudis, G.: Objective identification of cloud regimes in the
Tropical Western Pacific, Geophys. Res. Lett., 30, 2082,
https://doi.org/10.1029/2003GL018367, 2003. a
Kay, J. E., Wall, C., Yettella, V., Medeiros, B., Hannay, C., Caldwell, P., and
Bitz, C.: 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
King, M. D., Menzel, W. P., Kaufman, Y. J., Tanré, D., Gao, B. C.,
Platnick, S., Ackerman, S. A., Remer, L. A., Pincus, R., and Hubanks, P. A.:
Cloud and aerosol properties, precipitable water, and profiles of
temperature and water vapor from MODIS, IEEE T. Geosci.
Remote, 41, 442–456, https://doi.org/10.1109/TGRS.2002.808226, 2003. 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
Lana, A., Bell, T. G., Simó, R., Vallina, S. M., Ballabrera-Poy, J.,
Kettle, A. J., Dachs, J., Bopp, L., Saltzman, E. S., Stefels, J., Johnson,
J. E., and Liss, P. S.: An updated climatology of surface dimethlysulfide
concentrations and emission fluxes in the global ocean, Global
Biogeochem. Cy., 25, GB1004, https://doi.org/10.1029/2010GB003850, 2011. a
Leinonen, J., Lebsock, M. D., Oreopoulos, L., and Cho, N.: Interregional
differences in MODIS-derived cloud regimes, J. Geophys. Res.,
121, 11648–11665, https://doi.org/10.1002/2016JD025193, 2016. a
Liss, P. S. and Merlivat, L.: Air-Sea Gas Exchange Rates: Introduction and
Synthesis, in: The Role of Air-Sea Exchange in Geochemical Cycling, edited
by: Buat-Ménard, P., Springer Netherlands, Dordrecht, 113–127,
https://doi.org/10.1007/978-94-009-4738-2_5, 1986. a
Listowski, C., Delanoë, J., Kirchgaessner, A., Lachlan-Cope, T., and King, J.: Antarctic clouds, supercooled liquid water and mixed phase, investigated with DARDAR: geographical and seasonal variations, Atmos. Chem. Phys., 19, 6771–6808, https://doi.org/10.5194/acp-19-6771-2019, 2019. a
Mace, G. G. and Protat, A.: Clouds over the Southern Ocean as observed from
the R/V Investigator during CAPRICORN. Part I: Cloud occurrence and phase
partitioning, J. Appl. Meteorol. Clim., 57,
1783–1803, https://doi.org/10.1175/JAMC-D-17-0194.1, 2018. a
Mace, G. G., Protat, A., Humphries, R. S., Alexander, S. P., McRobert, I. M.,
Ward, J., Selleck, P., Keywood, M., and McFarquhar, G. M.: Southern Ocean
Cloud Properties Derived From CAPRICORN and MARCUS Data, J.
Geophys. Res.-Atmos., 126, e2020JD033368, https://doi.org/10.1029/2020JD033368, 2021. a
Mann, G. W., Carslaw, K. S., Spracklen, D. V., Ridley, D. A., Manktelow, P. T., Chipperfield, M. P., Pickering, S. J., and Johnson, C. E.: Description and evaluation of GLOMAP-mode: a modal global aerosol microphysics model for the UKCA composition-climate model, Geosci. Model Dev., 3, 519–551, https://doi.org/10.5194/gmd-3-519-2010, 2010. a
Mann, G. W., Carslaw, K. S., Ridley, D. A., Spracklen, D. V., Pringle, K. J., Merikanto, J., Korhonen, H., Schwarz, J. P., Lee, L. A., Manktelow, P. T., Woodhouse, M. T., Schmidt, A., Breider, T. J., Emmerson, K. M., Reddington, C. L., Chipperfield, M. P., and Pickering, S. J.: Intercomparison of modal and sectional aerosol microphysics representations within the same 3-D global chemical transport model, Atmos. Chem. Phys., 12, 4449–4476, https://doi.org/10.5194/acp-12-4449-2012, 2012. 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
McDonald, A. J., Cassano, J. J., Jolly, B., Parsons, S., and Schuddeboom, A.:
An automated satellite cloud classification scheme using self-organizing
maps: Alternative ISCCP weather states, J. Geophys. Res.,
121, 13009–13030, https://doi.org/10.1002/2016JD025199, 2016. a
McFarquhar, G. M., Bretherton, C. S., Marchand, R., Protat, A., DeMott, P. J.,
Alexander, S. P., Roberts, G. C., Twohy, C. H., Toohey, D., Siems, S., Huang,
Y., Wood, R., Rauber, R. M., Lasher-Trapp, S., Jensen, J., Stith, J. L.,
Mace, J., Um, J., Järvinen, E., Schnaiter, M., Gettelman, A., Sanchez,
K. J., McCluskey, C. S., Russell, L. M., McCoy, I. L., Atlas, R. L., Bardeen,
C. G., Moore, K. A., Hill, T. C. J., Humphries, R. S., Keywood, M. D.,
Ristovski, Z., Cravigan, L., Schofield, R., Fairall, C., Mallet, M. D.,
Kreidenweis, S. M., Rainwater, B., D’Alessandro, J., Wang, Y., Wu, W.,
Saliba, G., Levin, E. J. T., Ding, S., Lang, F., Truong, S. C. H., Wolff, C.,
Haggerty, J., Harvey, M. J., Klekociuk, A. R., and McDonald, A.:
Observations of Clouds, Aerosols, Precipitation, and Surface Radiation over
the Southern Ocean: An Overview of CAPRICORN, MARCUS, MICRE, and SOCRATES,
B. Am. Meteorol. Soc., 102, E894–E928,
https://doi.org/10.1175/BAMS-D-20-0132.1, 2021. a, b
Min, Q., Joseph, E., Lin, Y., Min, L., Yin, B., Daum, P. H., Kleinman, L. I., Wang, J., and Lee, Y.-N.: Comparison of MODIS cloud microphysical properties with in-situ measurements over the Southeast Pacific, Atmos. Chem. Phys., 12, 11261–11273, https://doi.org/10.5194/acp-12-11261-2012, 2012. a, b
Mulcahy, J. P., Jones, C., Sellar, A., Johnson, B., Boutle, I. A., Jones, A.,
Andrews, T., Rumbold, S. T., Mollard, J., Bellouin, N., Johnson, C. E.,
Williams, K. D., Grosvenor, D. P., and McCoy, D. T.: Improved Aerosol
Processes and Effective Radiative Forcing in HadGEM3 and UKESM1, J.
Adv. Model. Earth Sy., 10, 2786–2805,
https://doi.org/10.1029/2018MS001464, 2018. a
Mülmenstädt, J., Salzmann, M., Kay, J. E., Zelinka, M. D., Ma,
P.-L., Nam, C., Kretzschmar, J., Hörnig, S., and Quaas, J.: An
underestimated negative cloud feedback from cloud lifetime changes, Nat.
Clim. Change, 11, 508–513, https://doi.org/10.1038/s41558-021-01038-1, 2021. a
NASA: CERES Data Products, NASA [data set], https://ceres.larc.nasa.gov/data/, last access: 25 March 2022a. a
NASA: LAADS DAAC, NASA [data set], https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MCD06COSP_D3_MODIS/, last access: 25 March 2022b. a
Noble, S. R. and Hudson, J. G.: MODIS comparisons with northeastern Pacific in
situ stratocumulus microphysics, J. Geophys. Res.-Atmos., 120, 8332–8344, https://doi.org/10.1002/2014JD022785, 2015. a, b, c
Oreopoulos, L., Cho, N., Lee, D., Kato, S., and Huffman, G. J.: An examination
of the nature of global MODIS cloud regimes, J. Geophys.
Res.-Atmos., 119, 8362–8383, https://doi.org/10.1002/2013JD021409, 2014. a
Oreopoulos, L., Cho, N., Lee, D., and Kato, S.: Radiative effects of global
MODIS cloud regimes, J. Geophys. Res.-Atmos., 121,
2299–2317, https://doi.org/10.1002/2015JD024502, 2016. a, b
Painemal, D. and Zuidema, P.: Assessment of MODIS cloud effective radius and
optical thickness retrievals over the Southeast Pacific with VOCALS-REx in
situ measurements, J. Geophys. Res.-Atmos., 116, D24206,
https://doi.org/10.1029/2011JD016155, 2011. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn.
Res., 12, 12, 2825–2830, 2011. a
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Patrick Hofmann,
R. J.: Reconciling simulated and observed views of clouds: MODIS, ISCCP, and
the limits of instrument simulators, J. Climate, 25, 4699–4720,
https://doi.org/10.1175/JCLI-D-11-00267.1, 2012. a
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant,
B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway,
W. L., and Riedi, J.: The MODIS Cloud Optical and Microphysical Products:
Collection 6 Updates and Examples From Terra and Aqua, IEEE T.
Geosci. Remote, 55, 502–525, https://doi.org/10.1109/TGRS.2016.2610522,
2017. a, b, c, d, e
Protat, A., Schulz, E., Rikus, L., Sun, Z., Xiao, Y., and Keywood, M. D.:
Shipborne observations of the radiative effect of Southern Ocean clouds,
J. Geophys. Res.-Atmos., 122, 318–328,
https://doi.org/10.1002/2016JD026061, 2017. a
Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020. a
Schuddeboom, A., McDonald, A. J., Morgenstern, O., Harvey, M., and Parsons, S.:
Regional Regime-Based Evaluation of Present-Day General Circulation Model
Cloud Simulations Using Self-Organizing Maps, J. Geophys.
Res.-Atmos., 123, 4259–4272, https://doi.org/10.1002/2017JD028196, 2018. a, b, c, d, e, f, g, h, i
Simmons, J. B., Humphries, R. S., Wilson, S. R., Chambers, S. D., Williams, A. G., Griffiths, A. D., McRobert, I. M., Ward, J. P., Keywood, M. D., and Gribben, S.: Summer aerosol measurements over the East Antarctic seasonal ice zone, Atmos. Chem. Phys., 21, 9497–9513, https://doi.org/10.5194/acp-21-9497-2021, 2021. a
Sporre, M. K., O'Connor, E. J., Håkansson, N., Thoss, A., Swietlicki, E., and Petäjä, T.: Comparison of MODIS and VIIRS cloud properties with ARM ground-based observations over Finland, Atmos. Meas. Tech., 9, 3193–3203, https://doi.org/10.5194/amt-9-3193-2016, 2016. a, b
Tselioudis, G., Rossow, W., Zhang, Y., and Konsta, D.: Global weather states
and their properties from passive and active satellite cloud retrievals,
J. Climate, 26, 7734–7746, https://doi.org/10.1175/JCLI-D-13-00024.1, 2013. a
Tselioudis, G., Rossow, W. B., Jakob, C., Remillard, J., Tropf, D., and Zhang,
Y.: Evaluation of Clouds, Radiation, and Precipitation in CMIP6 Models Using
Global Weather States Derived from ISCCP-H Cloud Property Data, J.
Climate, 34, 7311–7324, https://doi.org/10.1175/JCLI-D-21-0076.1, 2021. a, b, c
van Marle, M. J. E., Kloster, S., Magi, B. I., Marlon, J. R., Daniau, A.-L., Field, R. D., Arneth, A., Forrest, M., Hantson, S., Kehrwald, N. M., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Yue, C., Kaiser, J. W., and van der Werf, G. R.: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015), Geosci. Model Dev., 10, 3329–3357, https://doi.org/10.5194/gmd-10-3329-2017, 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, b
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, 2017. a
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Elsevier, 3rd
edn., ISBN 978-0-12-815823-4, https://doi.org/10.1016/B978-0-12-385022-5.00026-9, 2011. a
Williams, K. D. and Tselioudis, G.: GCM intercomparison of global cloud
regimes: Present-day evaluation and climate change response, Clim.
Dynam., 29, 231–250, https://doi.org/10.1007/s00382-007-0232-2, 2007. a, b
Wilson, D. R., Bushell, A. C., Kerr-Munslow, A. M., Price, J. D., and
Morcrette, C. J.: PC2: A prognostic cloud fraction and condensation scheme.
I: Scheme description, Q. J. Roy. Meteor.
Soc., 134, 2093–2107, https://doi.org/10.1002/qj.333, 2008. a
Wood, N., Staniforth, A., White, A., Allen, T., Diamantakis, M., Gross, M.,
Melvin, T., Smith, C., Vosper, S., Zerroukat, M., and Thuburn, J.: An
inherently mass-conserving semi-implicit semi-Lagrangian discretization of
the deep-atmosphere global non-hydrostatic equations, Q. J.
Roy. Meteor. Soc., 140, 1505–1520, https://doi.org/10.1002/qj.2235,
2014. a
Woodward, S.: Modeling the atmospheric life cycle and radiative impact of
mineral dust in the Hadley Centre climate model, J. Geophys.
Res.-Atmos., 106, 18155–18166, https://doi.org/10.1029/2000JD900795,
2001. 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, b
Zhang, K., Wan, H., Liu, X., Ghan, S. J., Kooperman, G. J., Ma, P.-L., Rasch, P. J., Neubauer, D., and Lohmann, U.: Technical Note: On the use of nudging for aerosol–climate model intercomparison studies, Atmos. Chem. Phys., 14, 8631–8645, https://doi.org/10.5194/acp-14-8631-2014, 2014. a
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(10216 KB) - Full-text XML
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.
Climate models have difficulty simulating Southern Ocean clouds, impacting how much sunlight...
Altmetrics
Final-revised paper
Preprint