One of the major known problems in present-day global climate models is an excess in the absorbed short-wave (SW) radiation over the Southern Ocean (SO) . Chapter 9 of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) points out that most of the fifth phase Coupled Model Intercomparison Project (CMIP5) models have a positive SW cloud radiative bias of magnitude of up to 20 W m-2 over the SO, suggesting that inadequately simulated clouds allow substantially too much sunlight to reach the ocean surface. Several studies have focused on the relation between various aspects of cloud representation in the model and radiation biases pronounced over the SO. , using cyclone compositing cluster analyses, suggest the need to increase the optical depth of the low-level clouds and improve the simulation of mid-level cloud regime, to help reduce the biases in the model. By modifying the shallow convection detrainment in their global climate model, showed that the resultant increase in the supercooled liquid clouds enable large reductions in long-standing climate model SW radiation biases. and point towards the significance of mixed-phase clouds and their representation in the models for better representation of SO. In another study by , the importance of ice microphysics parameterisation in determining the phase composition, and thus the liquid water content of the SO clouds is highlighted.

Discrepancies in the response of clouds to anthropogenic forcings are recognised as a leading reason for a persistent, large spread in the climate sensitivity throughout various generations of climate models . We thus conjecture that this model problem of cloud albedo bias over the SO contributes to this large spread, and thus solving it would increase confidence in projections of anthropogenic climate change .

In the present study, we investigate the role of parameters involved in atmospheric ice formation within a global climate model in causing the above-mentioned SW radiation bias.

We define the SO region as the latitudinal band between 50 and 70∘ S in this study. The control climate model used in this study is the most recent version of the Met Office's Unified Model, GA7.1 with modified microphysics scheme for riming process and several other scientific changes. Appendix A summarises the scientific setup for this model version. The resolution used here is N96L85 (i.e. a horizontal resolution of 1.875∘×1.25∘ and 85 terrain-following hybrid-height levels extending to 85 km of altitude). It uses the “ENDGAME” dynamical core with a semi-implicit semi-Lagrangian formulation to solve the non-hydrostatic, fully compressible deep-atmosphere equations of motion

Figure  represents the anomaly in the annual and DJF mean distributions of ice water path (IWP) and liquid water path (LWP) for stratocumulus boundary layer clouds in the model in various experiments with respect to the control model, for the Southern Hemisphere (SH). There are seven boundary layer types that have been identified in the model based on the surface stability and capping cloud . As our focus is mostly on the stratocumulus boundary-layer-type clouds in this study, the cloud types considered in this figure are type 2 indicates boundary layer with stratocumulus over a stable near-surface layer, type 3 indicates well-mixed boundary layer, and type 4 indicates unstable boundary layer with a decoupled stratocumulus (DSC) layer not over cumulus. The IWP and LWP are calculated collectively over these types. The other cloud types in the model are those of stable boundary layer (type 1), boundary layer with decoupled stratocumulus layer over cumulus (type 5), cumulus-capped boundary layer (type 6) and shear-dominated unstable layer (type 7). A similar analysis for the non-stratocumulus cloud types has been provided in the Supplement (Figs. S1 and S2) for annual and DJF means.

From Fig. a and b, it is evident that there is a noticeable decrease in the IWP in the stratocumulus boundary layer clouds as a result of modified microphysics, which is captured in all sensitivity experiments in both annual and DJF means. The experiment, c_tnuc=-40 (solid black line in Fig. a and b) shows the maximum response and the experiment cap (solid red line in Fig. a and b) has the minimum decrease in IWP with respect to the control model. Conforming to the decrease in the IWP, there is a corresponding increase in the LWP as well, over the SO region (Fig. c and d). However, the response of LWP is more or less similar in all the three experiments with respect to the control model. It is because modification to capacitance value affects both the liquid and ice water contents, while changes to nucleation temperature will have an impact predominantly on IWP. Even any small sensitivity on LWP due to the changes in nucleation temperatures will not have much impact on the zonally averaged stratocumulus types, as the effects are mostly restricted to the shallow boundary types and hence not evident for Fig. . Thus, experiments where both capacitance and nucleation temperature are modified will have an added impact on the IWP.

Figure  shows the zonal-mean changes in the annual-mean distributions of various radiative fluxes in the model for the SH. Upward fluxes are used for all TOA figures and downward fluxes for surface figures. In all the model experiments, there is a general decrease in the outgoing LW flux at the TOA in the SO region (solid lines in Fig. a) with respect to the control model. This is accompanied by a corresponding increase in the outgoing SW flux at the TOA (dotted lines in Fig. b), indicating that in all model experiments the planetary albedo has increased versus the control. Except in cap, the decrease in LW radiation at the TOA, in absolute terms, is larger than the increase in SW TOA over the SO. This is visible in the distribution of net radiation at the TOA (i.e. LW plus SW at TOA) (dashed red line in Fig. c). For the cap experiment, there is an increase in the net outgoing TOA radiation, whereas for the c_tnuc=-40 and c_tnuc=-20 experiments, it shows a decrease over the SO region.

The surface distributions of the radiative fluxes are represented in Fig. d to f. In all the experiments, the net downward LW radiation at the surface shows an increase over the SO (solid lines in Fig. d) with respect to the control model. The corresponding SW component shows a decrease over SO (dotted lines in Fig. e). The distribution of anomaly in the radiative heat fluxes with respect to the control model is represented by dashed lines in Fig. f. It primarily represents the difference between total net downward surface radiation and total heat flux at the surface, i.e. (incoming LW plus SW at surface) minus (sensible heat flux plus latent heat flux). Although there is an improvement (i.e. reduction) in the downward SW component (dotted lines in Fig. e), due to the compensating increase in the LW component (solid lines in Fig. d), there is a net increase in the heat flux into the surface over the SO (dashed lines in Fig. f) in almost all experiments. However, the net radiative heat flux shows a tendency of slight decrease over SO for the cap experiment with respect to the control model (dashed green line in Fig. f).

Figure shows the distributions of various radiative fluxes in the model for the SH for the DJF season. The radiative fluxes show a more pronounced response during the austral summer season. The net radiative flux at TOA (dashed lines in Fig. c) shows an increase over the SO for all the experiments unlike the annual-mean distribution where only the cap experiment shows an increase. Similarly, the net radiative heat flux at the surface (dashed lines in Fig. f) shows a general decrease over the SO in all the experiments with respect to the control model for the DJF season, whereas in annual mean, only the cap experiment showed a decrease in net surface flux.

Figure represents the difference between the model experiments and observational data for annual mean. It is mainly intended to provide a reference for the model behaviour in terms of radiative fluxes. By comparing Figs.  and , it can be seen that it is the LW component that shows similar signs of response (solid lines in Figs. a, d and a, d) compared to SW for SO region. But the net fluxes show mostly similar signs of response in both TOA (Figs. c and c) and surface (Figs. f and f) except for the net TOA flux in the cap experiment (dashed red line in Fig. c).

Figure shows the zonally averaged annual and DJF mean distributions of the anomaly in the SW cloud radiative effect (SW CRE) between different model experiments with respect to the control model. The SW CRE shows the impact of cloud on TOA SW flux and is calculated by taking the anomaly of TOA SW flux between the clear-sky and all-sky conditions. The reduction in the SW radiative flux over SO is more pronounced in the DJF season (Fig. b) with the c_tnuc=-40 experiment showing the minimum reduction in SW CRE compared to the control model.

Figure shows the annual and DJF mean spatial distributions of the anomaly in SW CRE between different model experiments with respect to the control model. It is evident from Fig.  that there is a general improvement (i.e. a reduction) in the SW CRE over the SO region in all three experiments compared to the control model. The reduction is more pronounced in the cap experiment (Fig. a and d). The response in c_tnuc=-40 is the minimum (Fig. b and e). As expected, the response is more robust for the DJF season in all experiments with respect to the control model (Fig. d to f).

Figure  shows the spatial distribution of TOA SW CRE anomaly between various model simulations with respect to the CERES TOA data. As evident from Fig. a, the control model does have an excess in the absorbed SW radiation over the SH high latitudes like many other global models. Comparing Fig. a with b–d, it can be seen that the changes in microphysics parameterisation have improved the SW biases over the SO. In general, there is an increase in the reflected SW radiation over the SO compared to the control model. The values for annual-mean SW CRE biases in the model experiments (in comparison to both the control model as well as observational data) have been summarised in Table S1 of the Supplement.

It is to be noted that the tropical regions do not show much reduction in the SW CRE in response to the capacitance changes. While the SO does show significant reduction in SW CRE with respect to the control model, especially for the cap experiment (Fig. a and d), the tropical regions still show an increase. In an earlier study by , using a similar version of the control model (numerical weather prediction (NWP) configuration), it has been shown that for tropics and subtropics there is a general tendency by the model to overpredict the LWP in response to microphysics modifications. Increasing the stratiform cloud LWP will cause more SW radiation to be reflected back to space. But over the SO, this effect is beneficial because the control model already has a large negative bias in outgoing SW radiation in that region. Basically, in tropics or quasi-steady-state features like that of the fronts, the capacitance does not have much of an impact compared to more dynamic sites like that of supercooled liquid clouds .

Generally, the impact of ice nucleation temperature experiments (c_tnuc=-40 and c_tnuc=-20) on fluxes is more mixed than using capacitance change alone (cap). The detrimental effects due to changes in the ice nucleation temperature could be mostly attributed to the changes in the vertical distribution of clouds affecting not just the low clouds but also the high clouds. By changing the nucleation temperature, essentially the level at which freezing occurs is modified. When the freezing of water in lower levels is delayed, it results in cirrus clouds at higher atmospheric levels. This can change the high cloud characteristics thus affecting both LW and SW.

Errors in the representation of ice clouds is one of the major shortcomings in many of the present-day global climate models and this can have a significant influence on the global climate through their effects on the Earth's radiation budget . These errors are mostly coupled to an underestimation of supercooled liquid clouds and are of particular importance in the SO regions that are characterised by abundant supercooled liquid clouds . When ice and supercooled liquid coexist, the ice grows at the expense of the liquid by the Wegener–Bergeron–Findeisen (WBF) mechanism . Acknowledging the complexities in representing the many possible background microphysical processes that are responsible for this in a global climate model, the primary idea of modifying the shape parameter of ice crystals is to reduce the rate of depositional growth of ice particles. This reduction essentially slows down the deposition growth of ice crystals, which leaves more water vapour to be available for condensation into liquid phase particles. By lowering the value of capacitance to 0.5×d, we model a decrease in the IWP and an associated increase in the LWP over the SO region (Fig. ). As a result of this increase in LWP, the outgoing SW fluxes are increased (dotted lines in Figs. b and b), i.e. an increased LWP corresponds to brighter clouds reflecting more sunlight. This results in a decrease of the downwelling short-wave radiation reaching the surface.

Our choice of 0.5×d for capacitance is based on theory and observational studies . The control model has improved in terms of SO cloud albedo bias with this value than with the default value of 1.0×d. However, as seen in Fig., even though the SO has shown signs of improvement, biases still remain especially in the tropics. As already mentioned before, previous studies using a similar NWP configuration of the control model have suggested that the value of depositional capacitance is important mostly for non-equilibrium situations like mixed phase, whereas for quasi-steady frontal states and tropics, capacitance might not be very significant . Therefore, even if the same value for capacitance could be applied globally, the only regions where it will make noticeable differences are those of the mixed-phase cloud regions (like SO).

Earlier studies have also suggested that some cloud microphysics parameterisations produce unrealistically bright clouds, especially over the Northern Hemisphere (NH) . In our control model, the SW biases over the NH were smaller than those over the SO and further brightening of modelled NH clouds is undesirable . While the changes to nucleation temperature have a significant impact on the tropics as well, the capacitance changes are more localised to the high latitudes.

Our study shows that while modifying the capacitance is clearly benefiting the SO region SW radiative fluxes, there is some compensating effect from the LW TOA fluxes. As already discussed, the SW anomaly is thought to be mostly due to a lack of liquid clouds in the atmosphere. By targeting the capacitance, the loss of liquid in mixed-phase conditions is reduced, which results in an improvement in the outgoing SW TOA. However, this improvement in SW TOA always leads to an increase in the LW component due to thick clouds, which is unfortunately unavoidable. The annual-mean biases in TOA radiative fluxes along with RMSE values in comparison with observational data have been provided in Tables S2 and S3 of the Supplement. The SO RMSE values for SW TOA tend to be lower than global values as expected. Although the bias in SW TOA over SO has improved in general compared to the observational data, that improvement does not reflect much in the RMSE values. While an in-depth analysis of compensating errors from LW TOA is beyond the scope of this particular study, it is a significant aspect and provides an interesting outlook for future studies.

Also, several recent studies point towards the significance of INP for cloud phase . A further development of the research outlined here could be to make glaciation explicitly dependent on INP concentration to attend to the persisting model biases in other regions of the world as well. At present, in most global climate models, cloud phase is determined only by a threshold temperature like in our control model. , by combining a high-resolution NWP model with estimates of INP concentration over the SO, simulated clouds that are far more reflective than those in current global climate models, in better agreement with satellite observations.

In this study, we reduce the annual-mean SW radiation biases over SO in a recent version of the UK Met Office's Unified Model by up to ∼4 W m-2 and seasonally up to ∼8 W m-2 compared to the control model. This and other contemporary climate models are characterised by excess cloud ice causing biases in SW radiation which are especially pronounced over the SO. Here, we modify the capacitance or shape parameter which represents ice crystal shape and habit. In our sensitivity studies, we reduce this parameter from 1.0×d to 0.5×d (corresponding to any oblate sphere shape in general, where the horizontal axes are longer than the vertical axis and more representative of an aggregate or flat ice crystal) and thus delaying the depositional growth of ice particles. This leads to an increase in liquid water in stratiform clouds and consequently increases the outgoing SW over the SO. We also examine the impact of changing other temperature thresholds in the cloud microphysics scheme for the onset of heterogeneous ice production. Our analysis shows that the SW radiation bias has significantly reduced over the SO after the modification of these parameters. However, disparities still exist in other regions. INPs that are currently not represented in the cloud microphysics scheme might be a factor in this model behaviour. The fact that nucleation temperature changes currently is associated with the same effects globally is undesirable, it further motivates the future work to couple the nucleation temperature to a prognostic or the least a regionally specified INP concentration.