Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations

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.

Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: Disentangling the role of aerosol and dynamics with ground-based remote sensing

“…This result has significance for lidar-only or ceilometer datasets that are currently used for validation of climate models (e.g. Kuma et al, 2020). The occurrence of ice at these relatively warm temperatures is also in line with airborne in-situ observations over the Southern Ocean (Huang et al, 2017;D'Alessandro et al, 2019).…”

“…Young, 1999) and one of the cloudiest places on Earth (Haynes et al, 2011;Naud et al, 2014), but process understanding of clouds in that region is still limited. The reported biases in the solar radiation budget are attributed to shallow supercooled liquid topped clouds, which are insufficiently represented by current models (Bodas-Salcedo et al, 2014;Kay et al, 2016;Bodas-Salcedo et al, 2016;Kuma et al, 2020). These radiation biases affect estimates of sea surface temperature and surface precipitation, ultimately affecting the energy balance at the surface (Franklin et al, 2013;Hyder et al, 2018).…”

Constraining the Surface Flux of Sea Spray Particles From the Southern Ocean

“…It has been well established that there is currently a large shortwave radiation bias over the Southern Ocean (Bodas‐Salcedo et al, ). Observations of cloud base height from radiosondes and ceilometer measurements throughout this same voyage showed that the shortwave radiation bias is related to the lack of low‐level cloud and fog predicted within atmospheric models (Kuma et al, ). Therefore, it is reasonable to expect that even with an improved in‐cloud activation scheme (e.g., Grythe et al, ), FLEXPART‐WRF still would have underestimated the frequency of droplet activation events.…”

“…It is useful, however, to recall that clouds over the Southern Ocean are not very well represented within modern atmospheric models (Schuddeboom et al, ; Trenberth & Fasullo, ). Current era atmospheric models systematically underpredict the amount of low‐lying cloud and fog relative to the true cloud observed over the Southern Ocean (Kuma et al, ). Hence, even if a state‐of‐the‐art microphysical parameterization of in‐cloud scavenging had been present in FLEXPART‐WRF, it is likely that the scavenging of sea spray still would have been underestimated.…”

Spatial and Temporal Distribution of Sea Salt Aerosol Mass Concentrations in the Marine Boundary Layer From the Arctic to the Antarctic

“…Second, sea-air temperature difference is also a key factor in sea fog formation (Koračin et al, 2014). In regions where the value of SST-T2M is near zero (the air temperature is slightly lower than the water temperature), fog formation could be more common (Koračin et al, 2014;Kuma et al, 2020). This process could scavenge SSA masses from the boundary layer and potentially explain the phenomenon observed (Figure 3b).…”