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The third version of the Regional Atmosphere and Land (RAL3) science configuration is documented. Developed through international partnership, RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator when applied across timescales with kilometre and sub-km scale model grids. The RAL3 configuration represents a major advance compared to previous versions by delivering a common science definition suitable for application to tropical and mid-latitude regions. Developments within RAL3 include the introduction of a double-moment microphysics scheme and a bi-modal cloud scheme, replacing use of a single-moment scheme and different cloud schemes for mid-latitudes and tropics in previous versions. Updates have been implemented to the boundary layer scheme and a consolidation of land model settings to be more consistent with Global Atmosphere and Land (GAL) science configurations. Physics developments aimed to address priorities for model performance improvement identified by users. This paper documents the RAL3 science configuration, including a series of iterative revisions delivered since its first release, and their characteristics. Evidence is provided from the variety of assessments of RAL3, relative to the previous version (RAL2). Collaborative development and evaluation across organisations has enabled evaluation across a range of domains, grid-spacing and timescales. The analysis indicates more realistic precipitation distributions; improved representation of clouds and of visibility; a continued trend to more realistic representation of convection; and reduced near-surface wind speeds, but a persistent cold temperature bias. Overall the convective-scale verification scores and climatological model distributions relative to observations improve for the majority of variables. Ensemble results show improvements to the spread-error relationship. User feedback from subjective assessment activities has also been positive. Differences between RAL3 revisions and RAL2 are further illustrated through process-based analysis of a convective system over the UK. The latest RAL3 configuration (RAL3.3) is therefore recommended for research, operational numerical weather prediction and climate production at km and sub-km scales. [ABSTRACT FROM AUTHOR]

The evaluation and quantification of Southern Ocean cloud–radiation interactions simulated by climate models are essential in understanding the sources and magnitude of the radiative bias that persists in climate models for this region. To date, most evaluation methods focus on specific synoptic or cloud-type conditions that do not consider the entirety of the Southern Ocean's cloud regimes at once. Furthermore, it is difficult to directly quantify the complex and non-linear role that different cloud properties have on modulating cloud radiative effect. In this study, we present a new method of model evaluation, using machine learning that can at once identify complexities within a system and individual contributions. To do this, we use an XGBoost (eXtreme Gradient Boosting) model to predict the radiative bias within a nudged version of the Australian Community Climate and Earth System Simulator – Atmosphere-only model, using cloud property biases as predictive features. We find that the XGBoost model can explain up to 55 % of the radiative bias from these cloud properties alone. We then apply SHAP (SHapley Additive exPlanations) feature importance analysis to quantify the role each cloud property bias plays in predicting the radiative bias. We find that biases in the liquid water path are the largest contributor to the cloud radiative bias over the Southern Ocean, though important regional and cloud-type dependencies exist. We then test the usefulness of this method in evaluating model perturbations and find that it can clearly identify complex responses, including cloud property and cloud-type compensating errors. [ABSTRACT FROM AUTHOR]

The supersaturation equation for a vertically moving adiabatic cloud parcel is analyzed. The effects of turbulent updrafts are incorporated in the shape of a stochastic Lagrangian model, with spatial and time correlations expressed in terms of turbulent kinetic energy. Using the Fokker–Planck equation, the steady-state probability distributions of supersaturation are analytically computed for a number of approximations involving the time-scale separation between updraft fluctuations and phase relaxation, and droplet or ice particle size fluctuations. While the analytical results are presented in general for single-phase clouds, the calculated distributions are used to compute mixed-phase cloud properties—mixed fraction and mean liquid water content in an initially icy cloud—and are argued to be useful for generalizing and constructing new parameterization schemes. Significance Statement: Supersaturation is the fuel for the development of clouds in the atmosphere. In this paper, our goal is to better understand the supersaturation budget of clouds embedded in a turbulent environment by analyzing the basic equations of cloud microphysics. It is found that the turbulent characteristics of an air parcel substantially affect the cloud's supersaturation budget and hence its life cycle. This is also shown in the context of mixed-phase clouds where, depending on the turbulent regime, different liquid-to-ice ratios are found. Consequently, the theoretical approach of this paper is crucial to develop tools to parameterize small-scale atmospheric features, like clouds, into global circulation models to improve climate projections for the future. [ABSTRACT FROM AUTHOR]

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The evaluation and quantification of Southern Ocean cloud-radiation interactions simulated by climate models is essential in understanding the sources and magnitude of the radiative bias that persists in climate models for this region. To date, most evaluation methods focus on specific synoptic or cloud type conditions and are unable to quantitatively define the impact of cloud properties on the radiative bias whilst considering the system as a whole. In this study, we present a new method of model evaluation, using machine learning, that can at once identify complexities within a system and individual contributions. To do this, we use an XGBoost model to predict the radiative bias within a nudged version of the Australian Community Climate and Earth System Simulator -- Atmosphere-only Model, using cloud property biases as predictive features. We find that the XGBoost model can explain up to 55% of the radiative bias from these cloud properties alone. We then apply SHapley Additive exPlanations feature importance analysis to quantify the role each cloud property bias plays in predicting the radiative bias. We find that biases in liquid water path is the largest contributor to the cloud radiative bias over the Southern Ocean, though important regional and cloud-type dependencies exist. We then test the usefulness of this method in evaluating model perturbations and find that it can clearly identify complex responses, including cloud property and cloud-type compensating errors. [ABSTRACT FROM AUTHOR]

The evaluation and quantification of Southern Ocean cloud-radiation interactions simulated by climate models is essential in understanding the sources and magnitude of the radiative bias that persists in climate models for this region. To date, most evaluation methods focus on specific synoptic or cloud type conditions and are unable to quantitatively define the impact of cloud properties on the radiative bias whilst considering the system as a whole. In this study, we present a new method of model evaluation, using machine learning, that can at once identify complexities within a system and individual contributions. To do this, we use an XGBoost model to predict the radiative bias within a nudged version of the Australian Community Climate and Earth System Simulator – Atmosphere-only Model, using cloud property biases as predictive features. We find that the XGBoost model can explain up to 55 % of the radiative bias from these cloud properties alone. We then apply SHapley Additive exPlanations feature importance analysis to quantify the role each cloud property bias plays in predicting the radiative bias. We find that biases in liquid water path is the largest contributor to the cloud radiative bias over the Southern Ocean, though important regional and cloud-type dependencies exist. We then test the usefulness of this method in evaluating model perturbations and find that it can clearly identify complex responses, including cloud property and cloud-type compensating errors. [ABSTRACT FROM AUTHOR]