Climate processes

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).

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.

Abstract:

Plain Language Summary: Clouds have a big influence on Earth's climate. They affect how much sunlight is reflected or trapped, and how weather patterns form. But understanding clouds is very hard‐especially how they interact with tiny particles in the air called aerosols. These particles come from human activities and sources like wildfires, volcanoes. The way aerosols and clouds affect each other is one of the most uncertain parts of climate science. Because of this uncertainty, it's difficult to make accurate predictions about climate change and to give clear advice to decision‐makers. Scientists have made some progress in understanding aerosol‐cloud interactions, but more work is needed. With better tools, observations, and computer models, we can learn more over the next decade. However, because the climate is changing quickly and impacts are getting worse, we need faster action now. This summary explains the current knowledge on how aerosols and clouds interact, and why it's important to reduce the uncertainty. It also highlights what steps can help improve our understanding‐such as global collaboration and sharing knowledge between researchers, governments, and the public. Making faster progress in this area is key to better climate predictions, stronger climate policies, and lower risks for people and the planet.