Atmospheric processes

Abstract:

Large quantities of plastic pollution are accumulating at small island nations across the western Indian Ocean. Despite a historical focus on terrestrial inputs, recent research suggests that most pollution arriving at some islands may come from fishing and shipping activity. We use a 2D Lagrangian particle-tracking model, combined with satellite-tracked shipping and fishing data, to identify major fisheries and shipping lanes which are responsible for plastic debris beaching at the Seychelles. Virtual particles, representing plastic debris, are released monthly over multiple decades and are advected by currents from a 1/50°(∼2 km) regional ocean model. We find most fishing debris originates from within the Seychelles' own exclusive economic zone, and sources of shipping debris are concentrated along major shipping routes. Sources vary seasonally, due to wind-induced reversals of surface currents between monsoons. Finally, we find variation in the quantity and seasonality of debris accumulation on a sub-island scale. Therefore, higher-resolution models that resolve local currents and kilometre scale islands may play a vital role in clean-up and management efforts.

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:

Representing and quantifying uncertainty in physical parameterisations is a central challenge in weather and climate modelling, and approaches are often developed separately for different time‐scales. Here, we introduce a unified framework for analysing uncertainty in parameterisations across weather and climate regimes. Using the Lorenz 1996 system as a testbed for simplified chaotic dynamics, we quantify uncertainties in a subgrid‐scale parameterisation using a Bayesian neural network (BNN). This allows us to disentangle aleatoric uncertainty, arising from internal variability in the training data, and epistemic uncertainties, arising from poorly constrained parameters during training. At runtime, we sample uncertainties in line with stochastic approaches in weather models and perturbed‐parameter methods in climate models. On weather time‐scales, aleatoric uncertainty dominates, underscoring the value of stochastic parameterisations. On longer, climate time‐scales and under changing forcings, accounting for both types of uncertainty is necessary for well‐calibrated ensembles, with epistemic uncertainty widening the range of explored climate states, and aleatoric uncertainty promoting transitions between them. Constraining parameter uncertainty with short simulations reduces epistemic uncertainty and improves long‐term model behaviour under perturbed forcings. This framework links concepts from machine learning with traditional uncertainty quantification in earth system modelling, offering a pathway towards seamless treatment of uncertainty in weather and climate prediction.

Abstract:

Abstract We correct an error relating to the comparison of precipitation variability in coupled models versus models run with prescribed SSTs (“AMIP models”) and discuss what conclusions to draw from the corrected result.