| 09 Jul 2026
Implementing Marine Aerosol Ice Nucleation Parametrizations in the Unified Model: Mitigating the Cloud Radiative Bias During a Case Study Over the Southern Ocean
Abstract. Mixed-phase clouds over the Southern Ocean profoundly influence Earth's radiative balance, yet climate models persistently exhibit a positive surface shortwave radiation bias driven by over-glaciation in mixed-phase clouds. Addressing this bias requires accurate representation of ice-nucleating particles (INPs). This study presents the first online implementation of aerosol-aware marine INP parametrizations derived interactively from sea spray and marine organic aerosols within the high-resolution Unified Model. Using CAPRICORN-2 shipborne observations, we demonstrate that the default INP scheme overestimates INP concentrations by up to four orders of magnitude, causing a severely underestimated liquid water path. In contrast, empirical Antarctic and deterministic marine INP schemes reproduce the low INP concentrations typical of the pristine Southern Ocean, improving cloud and radiative properties. Crucially, microphysical and radiative responses to these INP reductions are strongly regime-dependent. In deep, pre-frontal mixed-phase clouds, suppressing INP concentrations effectively inhibits cloud glaciation, significantly enhancing supercooled liquid water and largely mitigating the surface shortwave radiation bias. However, in shallow, post-frontal stratocumulus clouds, altering INP parametrization yields negligible improvements. Radiosonde evaluations reveal this insensitivity is driven by several model deficits, including systematically smoothed boundary layer inversions leading to excessive dry air entrainment, exacerbated by underestimated cyclonic moisture transport. Consequently, these shallow clouds are thermodynamically starved of water vapor, rendering microphysical INP adjustments ineffective. Ultimately, fully resolving Southern Ocean cloud-radiation biases requires synergistic advancements in representing aerosols, boundary layer physics and large-scale meteorological forcings.
This study focuses on applying the UM regional model to simulate a Southern Ocean frontal clouds case to analyze the pre and post front mixed phase clouds with different parameterizations of Ice Nucleating Particles (INPs), with evaluations carried out using the CAPRICORN observation data. I suggest major revision for this current draft and some parts of the manuscript are too general for me to provide specific comments, but I am happy to review its second round.
This study shows comprehensive variables including hydrometer type, liquid/ice water path, temperature and humidity vertical profile, surface downwelling shortwave/longwave radiation, INPs number, surface meteorologies, etc. Technically, the analysis is cloud-phase microphysics oriented, and the focus on time series study intended to illustrate the development of the supercooled liquid clouds/mixed phase clouds as a system with simulated INPs as the changing factor. The strongest selling point of this paper, in my personal point of view, is scientific cross-validation, including the knowledge of potential related bias sources diagnoses and the comprehensive illustration of model performance using 2 regimes. The weak point of this paper, however, is its novelty and the depth of study.
To be a little specific, I acknowledge the credit of the research topic, methods, choice of model input, simulator, and most of the statistics shown in this paper. However, the novelty and depth of this study is expected to be further explored in the next round of review. For example, “the first online implementation of sea spray and marine organic aerosols as INP sources…”, the study didn’t fully explain the advantages of 1) online implementation of INP parameterization and 2) dynamical aerosol-aware parameterizations under this particular case. There indeed shows the various INP numbers and simulated hydrometers using different INP parameterizations as a result, and their accompanying statistics shows maybe a better general performance. But the authors didn’t show/ didn’t clearly illustrate the reason behind it. Similarly, I have a feeling, from both the introduction section and the description of figures, that immersion freezing should be the focus of your microphysics study. Again, the to-the-point diagnosis hasn’t been fully illustrated to tell the story out, which in theory would exactly be this high resolution UM RAL model’s advantage.
Similarly, the physics between the thermodynamics profiles, microphysics influenced by the INP schemes, and the resulting radiation fluxes are not dynamically connected. The hints have been listed all through the manuscript and the variables are reasonably chosen. Had this study been a simulation-observation class project final report, I would rate highly for its hard working, fruitful results, and comprehensive statistics. But for journal publication, I personally would like to see by changing the cloud liquid water into rain/ice crystals/snow/graupels, how do the INPs specifically indirectly influence the long/shortwave radiations. For the MIX period and SLW period specifically, what type of biases of Temperature and Humidity structure, adding in what extent of INP bias, by location or/and by population, resulted in how much cloud liquid water transfer into ice crystals/rain/graupel/supercooled liquid water through what processes? What are the above contributions from sea spray INPs and what are the corresponding results from the marine organic INPs. Did the sea ice representation affect the available water vapor source? Are the front location and low pressure center location and intensity well captured/nudged by the model/ERA5? Except for the hydrometer type, are the cloud cover/fraction represented well by the model in general with different INP schemes? I am happy to provide more specific comments in the future rounds of reviews when more in depth analysis is illustrated.
Minor:
Abstract can be more quantitative: try to avoid using confusing terms such as “default”, “high-resolution”,”starved of water vapor”. What is the default, explicitly mention the spatial resolution, quantify the underestimation of water vapor, suppressing INP number effectively inhibit cloud glaciation through what mechanism, etc..
Some statements have citations which are related and beneficial, but didn’t include the very original paper.
Some paper reviews can be more Southern Ocean oriented.
Suggest rephrasing Section 2.4, adding some concise and clear information about the lidar-radar merged products and the Silber et al. 2022 simulator and related hydrometer cluster method, just for reader’s convenience. Details can be listed in the Appendix. Similar for Section 2.2.4, maybe update this section with a Table for better vision and leave details in the Appendix. Similar for the model configurations, if a Table can better tell the input and schemes. The ultimate goal is to show the physical story line more clearly.
Suggest shortening the discussion part about potential uncertainties, instead focus on what the current data express with significance.