Philip Stier

Abstract:

<jats:p>Abstract. Cloud microphysics remains a major source of uncertainty in km-scale atmospheric models. While cloud-resolving models have advanced our understanding of cloud-climate interactions, their predictability remains limited. Most studies have examined either microphysics schemes or domain-size sensitivities, but their interactions are poorly understood. This study examines cloud structure and precipitation sensitivity to microphysics schemes and how they vary between regional and global configurations within a single, consistent modelling framework. We analyse three convection-permitting simulations over the Amazon: two regional runs employing single- and double-moment microphysics schemes and a global single-moment run, with all other configurations consistent. We find that cloud hydrometeor characteristics are sensitive to the microphysics scheme. Specifically, the double-moment scheme produces up to five times more graupel and twice as much rain, but half as much cloud water and one-fifth as much fog as the single-moment scheme. Despite these differences, precipitation, water vapour, and outgoing longwave radiation remain consistent across schemes, suggesting large-scale constraints primarily govern integrated quantities. Furthermore, domain configuration further amplifies sensitivities. The global simulation exhibits up to 150 % more fog and nearly double the cloud ice compared to the regional single-moment run, highlighting the role of large-scale circulation and lateral boundary conditions. These findings demonstrate that microphysics schemes primarily influence cloud processes, while the domain setup determines how these sensitivities manifest. Improved observational constraints and perturbed-parameter ensembles are therefore needed to evaluate model performance, assess the broader generalisability of these findings and separate tuning effects and structural uncertainty.</jats:p>

Abstract:

Abstract Next-generation high-resolution (km-scale) climate models promise unprecedented accuracy in climate projections, but realizing their potential requires robust methods to quantify how well simulations align with real-world observations. Average-based metrics conventionally used for climate model evaluation ignore the physics encoded in the fine-scale structures of km-scale simulations. To overcome this limitation, we propose a novel, statistically principled evaluation methodology based on the likelihood function of a generative image model. Our method provides a continuous similarity metric derived from the likelihood distribution of observation and simulation snapshots, which can redefine the evaluation, intercomparison, and parameter tuning of high-resolution climate models. We demonstrate the applicability and interpretability of this method by evaluating convective clouds simulated by two state-of-the-art global km-scale models, using their outgoing infrared radiation fields. This work establishes a scalable pathway toward observation-based evaluation of next-generation climate simulations.

Abstract:

<jats:p>Abstract. We present a new limited-area version of the aerosol–climate modeling system ICON coupled to HAM-lite. This new version is capable of simulating anthropogenic and natural aerosols and their climate effects in specific target regions. We demonstrate its flexibility and applicability through three case studies covering distinct aerosol regimes and processes: air pollution episodes in Central Europe, the emission and transport of sea salt aerosol in the Atlantic Arctic, and the simultaneous formation of smoke and desert dust plumes during the 2019–2020 Australian bushfire season. These case studies show the ability of the model to capture the regional-scale patterns and diurnal variability of the predominant aerosol types. They also indicate, however, systematic biases related to the simplified representation of aerosol emission, microphysics, and chemistry. The insights that we gained from these regional simulations will guide future developments of HAM-lite.</jats:p>