Abstract
Abstract. Southern Ocean (SO) shortwave (SW) radiation biases are a common problem in contemporary general circulation models (GCMs), with most models exhibiting a tendency to absorb too much incoming SW radiation. These biases have been attributed to deficiencies in the representation of clouds during the austral summer months, either due to cloud cover or cloud albedo being too low. The problem has been the focus of many studies, most of which utilised satellite datasets for model evaluation. We use multi-year ship-based observations and the CERES spaceborne radiation budget measurements to contrast cloud representation and SW radiation in the atmospheric component Global Atmosphere (GA) version 7.1 of the HadGEM3 GCM and the MERRA-2 reanalysis. We find that the prevailing bias is negative in GA7.1 and positive in MERRA-2. GA7.1 performs better than MERRA-2 in terms of absolute SW bias. Significant errors of up to 21 W m−2 (GA7.1) and 39 W m−2 (MERRA-2) are present in both models in the austral summer. Using ship-based ceilometer observations, we find low cloud below 2 km to be predominant in the Ross Sea and the Indian Ocean sectors of the SO. Utilising a novel surface lidar simulator developed for this study, derived from an existing Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) – active remote sensing simulator (ACTSIM) spaceborne lidar simulator, we find that GA7.1 and MERRA-2 both underestimate low cloud and fog occurrence relative to the ship observations on average by 4 %–9 % (GA7.1) and 18 % (MERRA-2). Based on radiosonde observations, we also find the low cloud to be strongly linked to boundary layer atmospheric stability and the sea surface temperature. GA7.1 and MERRA-2 do not represent the observed relationship between boundary layer stability and clouds well. We find that MERRA-2 has a much greater proportion of cloud liquid water in the SO in austral summer than GA7.1, a likely key contributor to the difference in the SW radiation bias. Our results suggest that subgrid-scale processes (cloud and boundary layer parameterisations) are responsible for the bias and that in GA7.1 a major part of the SW radiation bias can be explained by cloud cover underestimation, relative to underestimation of cloud albedo.
Highlights
Clouds are considered one of the largest sources of uncertainty in estimating global climate sensitivity (Boucher et al, 2013; Flato et al, 2014; Bony et al, 2015)
We evaluate the atmospheric component of Hadley Centre Global Environmental Model version 3 (HadGEM3), GA7.1 (Walters et al, 2019), and the reanalysis Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), using observations collected in the Southern Ocean (SO) on a number of voyages
We present this panel plot in order to evaluate how well GA7.1N and MERRA-2 are performing in terms of SW radiation bias in the SO relative to Clouds and the Earth’s Radiant Energy System (CERES)
Summary
Clouds are considered one of the largest sources of uncertainty in estimating global climate sensitivity (Boucher et al, 2013; Flato et al, 2014; Bony et al, 2015). Excess downward shortwave (SW) radiation in general circulation models (GCMs), with a bias over the SO of up to 30 W m−2, is a problem documented well by Trenberth and Fasullo (2010) and Hyder et al (2018) and has been the subject of many studies. Bodas-Salcedo et al (2014) evaluated the SW bias in a number of GCMs and found that a strong SW bias is a very common feature, leading to increased sea surface temperature (SST) in the SO and corresponding biases in the storm track position. Bodas-Salcedo et al (2012) studied the SO SW bias in the context of the Global Atmosphere (GA) 2.0 and 3.0 models and found that mid-topped and stratocumulus clouds are the dominant contributors to the bias The SW bias has been linked to large-scale model problems, such as the double Intertropical Convergence Zone (Hwang and Frierson, 2013), biases in the position of the midlatitude jet (Ceppi et al, 2012) and errors in the meridional energy transport (Mason et al, 2014). Bodas-Salcedo et al (2012) studied the SO SW bias in the context of the Global Atmosphere (GA) 2.0 and 3.0 models and found that mid-topped and stratocumulus clouds are the dominant contributors to the bias
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