Global storm-resolving models (GSRMs) are the upcoming global climate models. One of them is a 5-km Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Its high resolution means that parameterizations of convection and clouds, including subgrid-scale clouds, are omitted, relying on explicit simulation but still utilizing microphysics and turbulence parameterizations. Standard-resolution (10–100 km) models, which use convection and cloud parameterizations, have substantial cloud biases over the Southern Ocean (SO), adversely affecting radiation and sea surface temperature. The SO is dominated by low clouds, which cannot be observed accurately from space due to overlapping clouds, attenuation, and ground clutter. We evaluated SO clouds in ICON and the ERA5 and MERRA-2 reanalyses using about 2400 days of lidar observations and 2300 radiosonde profiles from 31 voyages and Macquarie Island station during 2010–2021, compared with the models using a ground-based lidar simulator. We found that ICON and the reanalyses underestimate the total cloud fraction by about 10 and 20%, respectively. ICON and ERA5 overestimate the cloud occurrence peak at about 500 m, potentially explained by their lifting condensation levels being too high. The reanalyses strongly underestimate fog or near-surface clouds, and MERRA-2 underestimates cloud occurrence at almost all heights. Outgoing shortwave radiation is overestimated in the reanalyses, implying a “too few, too bright” cloud problem. Thermodynamic conditions are relatively well represented, but ICON is less stable than observations, and MERRA-2 is too humid. SO cloud biases are a substantial issue in the GSRM, but it matches the observations better than the lower-resolution reanalyses.
Ship-based lidar evaluation of Southern Ocean clouds in the storm-resolving general circulation model ICON and the ERA5 and MERRA-2 reanalyses
Peter Kuma1, 2, 3, Frida A.-M. Bender1, 2, Adrian J. McDonald3, Simon P. Alexander4, 5, Greg M. McFarquhar6, 7, John J. Cassano8, 9, 10, Graeme E. Plank3, Sean Hartery3, 11, Simon Parsons3, 12, Sally Garrett13, Alex Schuddeboom3
1Department of Meteorology (MISU), Stockholm University, Stockholm, Sweden
2Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
3School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa/New Zealand
4Australian Antarctic Division, Kingston, Tasmania, Australia
5Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia
6Cooperative Institute of Severe and High Impact Weather Research and Operations, University of Oklahoma, Norman, OK, USA
7School of Meteorology, University of Oklahoma, Norman, OK, USA
8Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
9National Snow and Ice Data Center, University of Colorado, Boulder, CO, USA
10Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
11Department of Physics & Atmospheric Science, Dalhousie University, Halifax, Canada
12New South Wales Department of Planning and Environment, Sydney, New South Wales, Australia
13New Zealand Defence Force, Wellington, New Zealand
Abstract
- Note:
- manuscript in preparation
- Archive:
- Zenodo
- DOI:
- 10.5281/zenodo.14070222
BibTeX:
@article{kuma2024,
year={2024},
note={manuscript in preparation},
doi={10.5281/zenodo.14070222},
url={https://doi.org/10.5281/zenodo.14070222},
author={Kuma, Peter and Bender, Frida A.-M. and McDonald, Adrian J. and Alexander, Simon P. and McFarquhar, Greg M. and Cassano, John J. and Plank, Graeme E. and Hartery, Sean and Parsons, Simon and Garrett, Sally and Schuddeboom, Alex},
title={Ship-based lidar evaluation of Southern Ocean clouds in the storm-resolving general circulation model ICON and the ERA5 and MERRA-2 reanalyses}
}
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