Session R26: Geophysical Fluid Dynamics: General & Climate

Confinement on thermal convection from thermal plumes perspective

Despite the widespread occurrence of thermal convection in nature and artificial systems, we still lack a unified approach that integrates system geometry, fluid properties, and thermal forcing to characterize the transition from free to confined convective regimes. This transition is crucial for understanding energy transport and mixing across various environments, including convection in ample environments, like oceans and lakes, as well as in narrow systems, such as hydrothermal flows through fractures and engineering applications like heatsinks and microfluidics. In this work, we discuss the effect of confinement on flow structure and heat transfer from the standpoint of the smallest convective structure, asking: how tight the confinement is from a thermal plume perspective? Here, we introduce the degree of confinement Λ—the ratio of a thermal plume's thickness in an unbounded domain to the system's lateral extent—as a simple and universal metric to characterize convective regimes differing in flow dimensionality and time dependency, showing via laboratory experiments that transitions between these regimes are well-defined by Λ, offering a unified view of convection in closed systems from the plume's perspective.   

Determination of fluid-propagated fracture under biaxial loading conditions at varied rheological properties of the fracturing fluids in elastic medium

The hydraulic fracturing technique has been studied in conventional and unconventional reservoirs to enhance oil and gas recovery for the past few years. However, this technique is still challenging in unconventional reservoirs such as shale rocks. On the laboratory scale, very few studies have reported the deformation of layered rocks or fluid-induced fractures under biaxial loading. The present study uses lab-scale experiments and a numerical model to examine crack propagation due to fluid injection in isotropic (homogenous) and anisotropic (layered elastic medium/heterogeneous) material when loaded in different directions. In this current work, the biaxial loading system is particularly designed for the fracture propagation studies in analogue mediums. Gelatin of various compositions is used as an analogue medium owing to its transparency. We use water as a fracturing fluid in homogeneous and heterogeneous mediums, maintaining a 1 ml/min flow rate at a temperature of 15°C inside the matrix through a syringe pump.  In the present investigation, the parameters investigated for fracture propagation are the rheology of fracturing fluids, variable loads, and different solid matrices. Also, the mechanical properties of isotropic and anisotropic materials are determined. This lab scale analysis can be compared with the field-scale geological reservoirs using scaling laws in future investigations. The FEM-based numerical phase-field damage model is built to simulate the propagation of fluid fractures. Premilinary results are comparison with the experimentally observed fluid crack propagation to the simulation results.   

Fog Causality, Reversibility, and Forming Mechanisms.

The presence of fog significantly impacts transportation safety, agriculture, and daily planning. However, fog prediction remains challenging due to our limited understanding of the complex, multiscale processes that govern its formation, duration, and dissipation. The study aims to test whether there is causality in the fog cascade, to what extent this causality can be inferred from the turbulent series, and whether this reversibility/causality highlights the formation mechanism. To address this, we examined data from eight fog events with different formation mechanisms that were recorded and identified on Sable Island over the month of July 2022, as part of the Fog and Turbulence Interactions in the Marine Atmosphere (FATIMA) field campaign that was conducted in the Nova Scotia region. Here, we apply techniques from time series analysis including continuous wavelet transforms to explore the causality and statistical reversibility of fog events, which involve phase transitions that can be considered as non-reversible. Visibility is used as a surrogate for fog and turbulent kinetic energy is explored as a variable that can disrupt fog formation. This research provides valuable insights into the underlying dynamics of fog occurrence, paving the way for improved fog prediction and response strategies.

The Flow Structure and Dynamics of Unsteady Particle Sedimentation

Particles in fluid flows, such as plankton in the ocean, droplets in clouds, and suspended particulate matter in the atmosphere, are ubiquitous. Understanding their behavior is crucial for understanding large-scale geophysical processes. Both theoretical analyses and experimental studies underscore the significance of the history forces in the sedimentation of spherical particles within the unsteady Stokes flow regime. In this work, we focus on the unsteady flow structures generated by these particles. We employ particle image velocimetry to investigate these flow structures through a series of controlled experiments. During the initial sedimentation stages, a vortex emerges near the particle, representing a cross-sectional view of a three-dimensional vortex ring. As sedimentation progresses, the vortex core shifts away from the particle. Increasing the particle diameter induces larger inertial effects, altering the flow structure. We model these experimental observations using newly derived theoretical unsteady stream functions. Furthermore, we experiment with the simultaneous sedimentation of two spheres, varying their separation distance and characterizing the interactions between the particles within the flow field.   

On the multifractal and intermittent characteristics of tidal flows

We investigated the distinct directional variations of multifractal and intermittent characteristics of ebb and flood flow velocities at the Nodule Point, WA, tidal energy site, with complementary analysis at the East River, NY. To assess multifractality and intermittency, we compared scaling exponents of the structure function, distribution flatness, detrending moving average (DMA) analysis, multifractal detrended fluctuation analysis (MF-DFA), and high-order spectral moments. Our

findings indicate that tidal flows exhibit higher long-range dependence (LRD) and intermittent levels during ebb flow sections. Conversely, flood flow sections show increased multifractality and greater sensitivity to higher turbulent fluctuations. Through DMA and MF-DFA, we identified multiple fractal scalings within the tidal flow data, evidencing the complex multifractal nature of these flows. Scale-dependent intermittency levels, examined using spectral kurtosis, reveal a distinct pattern in tidal flow intermittency. This pattern contrasts with the monotonically increasing intermittency observed in wall-bounded and grid turbulence and displays a Gaussian-like PDF distribution for small-scale events at both tidal sites.   

Instability-driven two-dimensional turbulence

We study structure formation in two-dimensional turbulence driven by an external force, interpolating between linear instability forcing and random stirring, subject to nonlinear damping. The system exhibits four distinct branches of statistically stationary solutions: large-scale vortices, hybrid states with embedded shielded vortices (SVs) of either sign, and two symmetry-broken states composed of like SVs, a dense vortex gas and a hexagonal vortex crystal. These solutions coexist stably over a wide parameter range. The late-time evolution of the system from small-amplitude initial conditions is nearly self-similar, involving three phases: initial inverse cascade, random nucleation of SVs from turbulence and, once a critical number of vortices is reached, a phase of explosive nucleation of SVs, leading to a statistically stationary state. As the forcing strength decreases the vortex gas undergoes a sharp transition to a vortex crystal, and the vortex diffusivity drops to zero. The crystal can also decay via an inverse cascade resulting from the breakdown of shielding or insufficient nonlinear damping acting on SVs. Our study highlights the importance of forcing details in studies of two-dimensional turbulence.   

A non-intrusive framework for learning corrections to long time climate simulations from short time training data

Quantifying the risks of extreme weather events is becoming increasingly challenging due to our rapidly changing climate, and yet remains a critical step in the implementation of strategies to mitigate their impact on society. The vast range of scales relevant to the Earth’s turbulent atmosphere renders direct numerical simulation intractable over the multi-century time horizons needed for converged rare event statistics. On the other hand, coarse scale simulations that parametrize or insufficiently resolve the dynamics at the smallest scales generally suffer from an inability to generalize beyond the parameter regimes for which they were designed. This is because these “sub-grid” scales nontrivially affect the dynamics and statistics of large-scale phenomena in ways that are not universally understood. Here we present a general nonintrusive machine learning framework to correct the output of long-time coarse-resolution climate simulations. The framework -- which acts as a post-processing operation -- relies on training data pairs that have minimal chaotic divergence – namely a reference trajectory and a coarse simulation nudged towards that reference. Training on these specific trajectories allows our approach to generalize to unseen chaotic climate realizations, even when these are much longer than those seen in training. Furthermore, the post-processing nature ensures that our method is stable over indefinitely long-time horizons. We illustrate our approach on the E3SM climate model with 100km resolution, where with only 8 years of training data we can significantly reduce the error in global and regional 40-year statistics relative to ERA5 reanalysis data.   

Emulating conditional probability distribution of climate extremes via score-based diffusion models

Extreme events in the climate system, such as heat waves, hurricanes, and floods, have become more frequent and intense in recent decades. While Earth System Models (ESMs) provide comprehensive insights into these climate extremes, they are computationally expensive, especially when a large ensemble of simulations is required to quantify climate internal variability. Reduced-complexity models, or emulators, serve as speedy complements to ESMs, projecting key climate variables under various future scenarios. However, most existing emulators focus on predicting time-averaged quantities without estimating the uncertainty related to internal variability. Score-based diffusion models, one of the most widely adopted generative deep-learning models, offer a promising alternative by efficiently representing the high-dimensional joint probability distribution of local climate variables. Our training data are collected from the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. Given a snapshot of the monthly-mean temperature, our model predicts the distribution of daily precipitation and maximum temperature on spatially resolved grids. The performance of the diffusion model is compared against canonical emulators such as linear pattern scaling. Despite being trained on only one future climate change scenario, the diffusion model consistently maintains high emulation accuracy across testing scenarios that were not included in the training data. We will also discuss the potential of diffusion models to extrapolate the tails of probability distributions beyond the available realizations of earth system simulations.