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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 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]

We use convective-scale simulations of monsoonal clouds to reveal a self-similar probability density function that underpins surface rainfall statistics. This density is independent of cloud-droplet number concentration and is unchanged by aerosol perturbations. It therefore represents an invariant property of our model with respect to cloud–aerosol interactions. For a given aerosol concentration, if the dependence of at least one moment of the rainfall distribution on cloud-droplet number is a known input parameter, then the self-similar density can be used to reconstruct the entire rainfall distribution to a useful degree of accuracy. In particular, we present both single-moment and double-moment reconstructions that are able to predict the responses of the rainfall distributions to changes in aerosol concentration. In doing so, we show that the seemingly high-dimensional space of possible aerosol-induced rainfall-distribution transformations can be parameterised by surprisingly few (at most 3) independent "degrees of freedom": the self-similar density and auxiliary information about two moments of the rainfall distribution. Comparisons to convection-permitting forecasts of mid-latitude weather and atmosphere-only global simulations show that the self-similar density is also independent of model physics and background meteorology. A theoretical explanation for this invariance is given, based on numerical results from a stochastic rainfall simulator. This suggests that, although aerosol indirect effects on any specific hydro-meteorological system may be multifarious in terms of rainfall changes and physical mechanisms, there may, nevertheless, be a universal constraint on the number of independent degrees of freedom needed to represent the dependencies of rainfall on aerosols. [ABSTRACT FROM AUTHOR]

The Earth system models (ESMs) that participated in the sixth Coupled Model Intercomparison Project (CMIP6) tend to simulate excessive cooling in surface air temperature (TAS) between 1960 and 1990. The anomalous cooling is pronounced over the Northern Hemisphere (NH) midlatitudes, coinciding with the rapid growth of anthropogenic sulfur dioxide (SO2) emissions, the primary precursor of atmospheric sulfate aerosols. Structural uncertainties between ESMs have a larger impact on the anomalous cooling than internal variability. Historical simulations with and without anthropogenic aerosol emissions indicate that the anomalous cooling in the ESMs is attributed to the higher aerosol burden in these models. The aerosol forcing sensitivity, estimated as the outgoing shortwave radiation (OSR) response to aerosol concentration changes, cannot well explain the diversity of pothole cooling (PHC) biases in the ESMs. The relative contributions to aerosol forcing sensitivity from aerosol–radiation interactions (ARIs) and aerosol–cloud interactions (ACIs) can be estimated from CMIP6 simulations. We show that even when the aerosol forcing sensitivity is similar between ESMs, the relative contributions of ARI and ACI may be substantially different. The ACI accounts for between 64 % and 87 % of the aerosol forcing sensitivity in the models and is the main source of the aerosol forcing sensitivity differences between the ESMs. The ACI can be further decomposed into a cloud-amount term (which depends linearly on cloud fraction) and a cloud-albedo term (which is independent of cloud fraction, to the first order), with the cloud-amount term accounting for most of the inter-model differences. [ABSTRACT FROM AUTHOR]

We use convective-scale simulations of monsoonal clouds to reveal a self-similar probability density function that underpins surface rainfall statistics. This density is independent of cloud-droplet number concentration and is unchanged by aerosol perturbations. It therefore represents an invariant property of our model with respect to cloud-aerosol interactions. For a given aerosol concentration, if the dependence of at least one moment of the rainfall distribution on cloud-droplet number is a known input parameter, then the self-similar density can be used to reconstruct the entire rainfall distribution to a useful degree of accuracy. In particular, we present both single-moment and double-moment reconstructions that are able to predict the responses of the rainfall distributions to changes in aerosol concentration. In doing so we show that the seemingly high-dimensional space of possible aerosol-induced rainfall-distribution transformations can be parametrized by a surprisingly small (at most three) independent 'degrees of freedom': the self-similar density, and auxiliary information about two moments of the rainfall distribution. This suggests that, although aerosol-indirect effects on any specific hydro-meteorological system may be multifarious in terms of rainfall changes and physical mechanisms, there may, nevertheless, be a universal constraint on the number of independent degrees of freedom needed to represent the dependencies of rainfall on aerosols. [ABSTRACT FROM AUTHOR]

Insufficient reflection of short-wave radiation especially over the Southern Ocean region is still a leading issue in many present-day global climate models. One of the potential reasons for this observed bias is an inadequate representation of clouds. In a previous study, we modified the cloud micro-physics scheme in the Unified Model and showed that choosing a more realistic value for the capacitance or shape parameter of atmospheric ice-crystals, in better agreement with theory and observations, benefits the simulation of short-wave radiation over the Southern Ocean by brightening the clouds. However, attempts to modify the cloud phase by directly adjusting the micro-physics process rates like capacitance tend to affect both the hemispheres symmetrically whereas we seek to brighten only the high-latitude Southern Hemisphere clouds. In this study we implement a simple prognostic parametrisation whereby the heterogeneous ice nucleation temperature is made to vary three-dimensionally as a function of the mineral dust distribution in the model. As a result, those regions with less dust number density would have lower nucleation temperature compared to the default global value of -10° C. By using mineral dust as an indicator for ice nucleating particles in the model, this parametrisation thus captures the impact of ice nucleating particles on the cloud distribution due to its general paucity over the Southern Ocean region. This approach thus improves the physics of the model with minimal complexity. [ABSTRACT FROM AUTHOR]

The present generation of global climate models is characterised by insufficient reflection of short-wave radiation over the Southern Ocean due to a misrepresentation of clouds. This is a significant concern as it leads to excessive heating of the ocean surface, sea surface temperature biases and subsequent problems with atmospheric dynamics. In this study, we modify cloud microphysics in a recent version of the Met Office's Unified Model and show that choosing a more realistic value for the shape parameter of atmospheric ice crystals, in better agreement with theory and observations, benefits the simulation of short-wave radiation. In the model, for calculating the growth rate of ice crystals through deposition, the default assumption is that all ice particles are spherical in shape. We modify this assumption to effectively allow for oblique shapes or aggregates of ice crystals. Along with modified ice nucleation temperatures, we achieve a reduction in the annual-mean short-wave cloud radiative effect over the Southern Ocean by up to ∼4 W m -2 and seasonally much larger reductions compared to the control model. By slowing the growth of the ice phase, the model simulates substantially more supercooled liquid cloud. [ABSTRACT FROM AUTHOR]

Convection-permitting simulations are used to understand the effects of cloud–aerosol interactions in a case of heavy rainfall over southern China. The simulations are evaluated using radar observations from the Southern China Monsoon Rainfall Experiment (SCMREX) and remotely sensed estimates of precipitation, clouds and radiation. We focus on the effects of complexity in cloud–aerosol interactions, especially the depletion and transport of aerosol material by clouds. In particular, simulations with aerosol concentrations held constant are compared with a fully cloud–aerosol-interacting system to investigate the effects of two-way coupling between aerosols and clouds on a line of organised deep convection. It is shown that the cloud processing of aerosols can change the vertical structure of the storm by using up aerosols within the core of line, thereby maintaining a relatively clean environment which propagates with the heaviest rainfall. This induces changes in the statistics of surface rainfall, with a cleaner environment being associated with less-intense but more-frequent rainfall. These effects are shown to be related to a shortening of the timescale for converting cloud droplets to rain as the aerosol number concentration is decreased. The simulations are compared to satellite-derived estimates of surface rainfall, a condensed-water path and the outgoing flux of short-wave radiation. Simulations for fewer aerosol particles outperform the more polluted simulations for surface rainfall but give poorer representations of top-of-atmosphere (TOA) radiation. [ABSTRACT FROM AUTHOR]

The present generation of global climate models is characterized by insufficient reflection of short-wave radiation over the Southern Ocean due to a misrepresentation of clouds. This is a significant concern as it leads to excessive heating of the ocean surface, sea surface temperature biases, and subsequent problems with atmospheric dynamics. In this study we modify cloud micro-physics in a recent version of the Met Office's Unified Model and show that choosing a more realistic value for the shape parameter of atmospheric ice-crystals, in better agreement with theory and observations, benefits the simulation of short-wave radiation. In the model, for calculating the growth rate of ice crystals through deposition, the default assumption is that all ice particles are spherical in shape. We modify this assumption to effectively allow for oblique shapes or aggregates of ice crystals. Along with modified ice nucleation temperatures, we achieve a reduction in the annual-mean short-wave cloud radiative effect over the Southern Ocean by up to 4 W/m2, and seasonally much larger reductions. By slowing the growth of the ice phase, the model simulates substantially more supercooled liquid cloud. We hypothesize that such abundant supercooled liquid cloud is the result of a paucity of ice nucleating particles in this part of the atmosphere. [ABSTRACT FROM AUTHOR]

Convection-permitting simulations are used to understand the effects of cloud-aerosol interactions on a case of heavy rainfall over south China. The simulations are evaluated using radar observations from the South China Monsoon Rainfall Experiment and remotely sensed estimates of precipitation, clouds and radiation. We focus on the effects of complexity in cloud-aerosol interactions, especially processing and transport of dissolved material inside clouds. In particular, simulations with aerosol concentrations held constant are compared with a fully coupled cloud-aerosol-interacting system to isolate the effects of processing on a line of organised-deep convection. It is shown that in-cloud processing of aerosols can change the vertical structure of squall lines thereby inducing changes in the statistics of surface rainfall. These effects are shown to be consistent with a modulation by aerosol of the timescale of the converting cloud-droplets to rain. [ABSTRACT FROM AUTHOR]

We describe Global Atmosphere 7.0 and Global Land 7.0 (GA7.0/GL7.0), the latest science configurations of the Met Office Unified Model (UM) and the Joint UK Land Environment Simulator (JULES) land surface model developed for use across weather and climate timescales. GA7.0 and GL7.0 include incremental developments and targeted improvements that, between them, address four critical errors identified in previous configurations: excessive precipitation biases over India, warm and moist biases in the tropical tropopause layer (TTL), a source of energy non-conservation in the advection scheme and excessive surface radiation biases over the Southern Ocean. They also include two new parametrisations, namely the UK Chemistry and Aerosol (UKCA) GLOMAP-mode (Global Model of Aerosol Processes) aerosol scheme and the JULES multi-layer snow scheme, which improve the fidelity of the simulation and were required for inclusion in the Global Atmosphere/Global Land configurations ahead of the 6th Coupled Model Intercomparison Project (CMIP6). In addition, we describe the GA7.1 branch configuration, which reduces an overly negative anthropogenic aerosol effective radiative forcing (ERF) in GA7.0 whilst maintaining the quality of simulations of the present-day climate. GA7.1/GL7.0 will form the physical atmosphere/land component in the HadGEM3–GC3.1 and UKESM1 climate model submissions to the CMIP6. [ABSTRACT FROM AUTHOR]

We describe Global Atmosphere 7.0 and Global Land 7.0 (GA7.0/GL7.0): the latest science configurations of the Met Office Unified Model and JULES land surface model developed for use across weather and climate timescales. GA7.0 and GL7.0 include incremental developments and targeted improvements that between them address four critical errors identified in previous configurations: excessive precipitation biases over India, warm/moist biases in the tropical tropopause layer, a source of energy non-conservation in the advection scheme and excessive surface radiation biases over the Southern Ocean. They also include two new parametrisations, namely the UKCA Glomap-mode aerosol scheme and the JULES multi-layer snow scheme, which improve the fidelity of the simulation and were required for the coupled climate models and Earth system models that will use these configurations in submissions to CMIP6. In addition, we describe the GA7.1 branch configuration, which reduces an overly negative anthropogenic aerosol effective radiative forcing in GA7.0, whilst maintaining the quality of simulations of the present-day climate. GA7.1/GL7.0 will form the physical atmosphere/land component in the HadGEM3-GC3.1 and UKESM1 climate models. [ABSTRACT FROM AUTHOR]