Philip Stier

Abstract:

<jats:p>Abstract. The rapidly growing volume of weather and climate data, both from models and observations, is increasing the pressure on data centers, restricting scientific analysis, and data distribution. For example, kilometre-scale climate models can generate petabytes of data per simulated month, making it generally infeasible to store all output. To address this challenge, numerous novel compression techniques have been proposed to ease data storage requirements. However, there exist no well-defined benchmarks for rigorously evaluating and comparing the performance of these compressors, including their impact on the data's properties. The lack of benchmarks makes it difficult to design and standardize compressors for weather and climate data, and for scientists to trust that compression errors have no significant impact on their analysis. Here, we address this gap by presenting ClimateBenchPress, a benchmark suite for lossy compression of climate data, which defines both data sets and evaluation techniques. The benchmark covers climate variables following various statistical distributions at medium to very high resolution in time and space, from both numerical models and satellite observations. To ensure a fair comparison between different compressors, each variable comes with a set of maximum error bound checks that the lossy compressors need to pass. By evaluating an initial set of baseline compressors on the benchmark, we gather practical insights for effective application of lossy compression. Our benchmark is open source and extensible: users can easily add new compressors, data sources, and evaluation metrics depending on their own specific use cases.</jats:p>

Abstract:

Our limited understanding of clouds is the dominant source of uncertainty in future climate predictions. Understanding how changing atmospheric environmental conditions constrain cloud organisational patterns and their radiative effects is key to understanding their impact on future climate change. We present CloudFlow, a flow matching model that is able to generate high-resolution cloud structures conditioned on coarse-scale atmospheric conditions. Our model generates realistic cloud structures that match the spectra and distributions of the original high-resolution scenes. CloudFlow introduces a new modeling regime to study how atmospheric environmental conditions impact cloud morphologies which will contribute to an improved understanding of cloud feedbacks, the cloud response to a changing climate and its effect on climate itself.

Abstract:

Deep convective clouds substantially modify the balance of shortwave and longwave radiative energy at the top of the atmosphere. Although in the present-day these effects approximately balance out, projected changes in deep convective clouds could alter the future top-of-atmosphere energy balance. Past studies have found relationships between convection and anvil clouds, but our understanding of how convection typically controls the properties and evolution of anvil clouds that determine anvil radiative effects remains incomplete, limiting our ability to explain or justify projected changes in cloud optical properties. This manuscript presents a new method to track the lifecycle of deep convective clouds and their convective cores in three-dimensional space in km-scale global climate models. An analysis of how convective organisation, intensity and area relate to anvil properties in the ICOsahedral Non-hydrostatic (ICON) model is then presented. Approximately 1000 deep convective clouds are tracked over one simulation week in the tropical Amazon region. We find that while both updraft intensity and area correspond to larger anvils, the correlation between convective area and anvil size is stronger than that between anvil size and updraft intensity. Updraft intensity was associated with a 4-fold increase in anvil extent when convective cores were larger, compared to when they were in the bottom 50th size percentile. This result could not be explained by associated changes in peak convective mass flux or organisation. These results indicate how changes in the frequency or typical size of convective updrafts may link to changes in anvil development, extent and, ultimately, radiative effects.

Abstract:

The radiative response of deep convective anvil clouds to anthropogenic aerosols is a major source of uncertainty. While aerosol-cloud interactions (ACI) in the convective core have been extensively studied, the microphysical mechanisms governing the full anvil lifecycle, from detrainment to dissipation, remain poorly constrained.This study examines the Cloud Radiative Effect (CRE) of deep convection through a microphysical process-rate lens. We perform three regional simulations with interactive aerosol using ICON-HAM-lite, comprising baseline, clean, and polluted runs. The simulations follow the TRACER-MIP protocol for a sea-breeze event over Houston, Texas. Using Lagrangian tracking with the tobac cloud tracking algorithm, we isolate individual convective cells and track their evolution from convective onset to the detrainment and dissipation of the resulting anvils. We then assess aerosol-cloud interactions over the lifecycle of the tracked cells by aligning their evolution with the onset of freezing, to ensure a consistent lifecycle comparison.Our results show that a 9-fold increase in aerosol concentration leads to a 2.5-fold increase in cloud droplet number concentration (CDNC). This suppresses warm-rain processes and enhances upward mass flux above the melting layer. As a result, it also lofts higher droplet concentrations, which can shape anvil characteristics by modulating the total ice surface area available for deposition and the net cross-section for riming. This creates a competition between enhanced riming, which promotes mass fallout, and increased vapour deposition, which sustains smaller ice crystals aloft. We conclude by investigating how these competing factors change the lifetime of the anvil and its net CRE.