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Linking the organization of precipitation extremes at sub-meso-alpha scales to surface wind fluctuations in a storm-resolving GCM 

(2026)

Authors:

Sadhitro De, Philip Stier

Abstract:

Convective systems exhibit a wide range of cloud and precipitation structures spanning spatial scales from a few kilometres to thousands of kilometres. While the organization of convection at the meso-alpha scale (200–2000 km) is relatively well-researched through observations and numerical modelling, much less is known about how convection organizes at smaller scales, down to a few kilometres, that are now accessible to kilometre-scale, storm-resolving models.To address this, we investigate the spatial organization of extreme precipitation in simulations of the storm-resolving model, ICON, coupled to the prognostic aerosol module, HAM-lite. Using month- long, kilometre-scale limited-area simulations over the Atlantic Intertropical Convergence Zone, conducted for the ORCESTRA measurement campaign period [1], we find that 99th-percentile precipitation extremes over the ocean exhibit robust scale-invariant organization across spatial scales from approximately 10 to 150 km, characterised by a fractal dimension of approximately 4/3.While individual convective updrafts are associated with strong surface convergence, their organisation at these scales is significantly influenced by cold pools which generate intense surface wind divergence. Consistent with this mechanism, grid points with large absolute values of surface wind divergence form spatial clusters that statistically resemble those of extreme precipitation. They tend to predominantly affect the intermittency of surface wind fluctuations, in a manner analogous to shocks in compressible turbulence. Building upon this analogy, we demonstrate that the surface wind fluctuations indeed exhibit a nearly-bifractal scaling — consistent with certain models of compressible turbulence [2] — and the scaling exponents of higher-order surface wind velocity structure functions appear to approach the co-dimension of the fractal set defined by the extreme precipitation events.This establishes a direct quantitative link between the spatial organization of precipitation extremes and surface wind fluctuations at sub–meso-alpha scales, highlighting implications for the development of simple yet physically grounded stochastic parameterizations of the latter in coarse- resolution GCMs. Furthermore, we assess the robustness of such organization to various climate- change and air pollution scenarios via perturbations to the prescribed sea-surface temperatures and aerosol emissions, respectively. References:[1] https://orcestra-campaign.org/intro.html[2] Mitra et al, Physical Review Letters 94, 194501 (2005).
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Quantifying and Constraining Aerosol Forcing Uncertainty: From Single-Model to Multi-Model Perturbed Parameter Ensembles

(2026)

Authors:

Hailing Jia, Duncan Watson-Parris, David Neubauer, Yusuf Bhatti, Michael Schulz, Leighton Regayre, Philip Stier, Johannes Quaas, Daniel Partridge, Ardit Arifi, Anne Kubin, Athanasios Nenes, Ulas Im, Nick Schutgens, Bastiaan van Diedenhoven, Sylvaine Ferrachat, Ulrike Lohmann, Ina Tegen, Alice Henkes, Otto Hasekamp

Abstract:

Changes in aerosols since the preindustrial era have altered the top-of-the-atmosphere radiation balance by directly scattering solar radiation and indirectly interacting with clouds, known as aerosol effective radiative forcing (ERFaer). ERFaer persistently remains one of the most uncertain components in global climate model simulations, due to the imperfect representations of aerosol and cloud properties and processes. Perturbed parameter ensembles (PPEs) are increasingly used to quantify these sources of uncertainty and to constrain models with observations.Here, we first present a single-model PPE using the ICON-A-HAM2.3 model, designed to identify key sources of ERFaer uncertainty. This PPE comprises 383 simulations for both preindustrial and present-day conditions, in which 42 parameters related to aerosol emissions, aerosol properties and processes, cloud microphysics, convection, and turbulence are perturbed simultaneously. Gaussian process emulators are trained on model outputs to enable efficient sampling of this high-dimensional parameter space. Our analysis focuses on uncertainty quantification and attribution for aerosol and cloud properties as well as ERFaer, along with comparisons against satellite observations from SPEXone/PACE and MODIS. Our results show a global mean ERFaer of −1.10 W m⁻² (5–95 percentile: −1.54 to −0.68 W m⁻²), with the overall uncertainty dominated by aerosol-related processes, particularly aerosol emissions.Building on this single-model framework, we further propose a Multi-Model PPE (MMPPE) initiative within the AeroCom Phase IV experiments. This multi-model approach allows us to simultaneously address structural and parametric uncertainties across models, providing a coordinated pathway toward reducing ERFaer uncertainty in current climate models. An overview of the MMPPE design and objectives will be presented.
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ICON coupled to HAM-lite 1.0 in limited-area mode: an efficient framework for targeted kilometer-scale simulations with interactive aerosols

(2026)

Authors:

Bernd Heinold, Philipp Weiss, Sadhitro De, Anne Kubin, Jason Müller, Fabian Senf, Philip Stier, Ina Tegen
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ClimateBenchPress (v1.0): A Benchmark for Lossy Compression of Climate Data

(2026)

Authors:

Tim Reichelt, Juniper Tyree, Milan Klöwer, Peter Dueben, Bryan N Lawrence, Allison H Baker, Sara Faghih-Naini, Torsten Hoefler, Philip Stier
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Physics-Constrained Reduced-Order Modeling of Collision-Coalescence with Advectable Embeddings: Monotonic Mass Partition Scheme

(2026)

Authors:

Kang-En Huang, Minghuai Wang, Philip Stier, Tobias Bischoff, Tim Reichelt, Yannian Zhu, Daniel Rosenfeld
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