Atmospheric processes

Abstract:

It is often difficult to attribute uncertainty and errors in atmospheric models to designated model components. This is because sub-grid parameterised processes interact strongly with the large-scale transport represented by the explicit model dynamics. We carry out experiments with prescribed large-scale dynamics and different sub-grid physics suites. This dataset has been constructed for the Model Uncertainty Model Intercomparison Project (MUMIP), in which each suite forecasts sub-grid tendencies at a 22km grid. The common dynamics is derived from a convection-permitting benchmark: an ICON DYAMOND experiment (2.5km grid). We compare four different physics suites for atmospheric models in an Indian Ocean experiment. We analyse their joint PDFs of precipitation and associated physics tendencies for a full month, where precipitation is used to diagnose uncertainty of convective activity. We find that all physics suites produce very similar precipitation amounts, with very high correlations between models, i.e.,  > 0.95 at the native grid. However, the convection-permitting benchmark is more dissimilar from each of the physics suites, with correlations of  ≈ 0.80. Similarly, we show that the vertically averaged physics tendencies in the free-troposphere are highly similar between the four physics suites, yet different if reconstructed for the benchmark. The water vapour sink is very closely linked with precipitation in the four physics suites. This suggests that the coarse-grid models are overconfident. We hypothese is that variation in unresolved convective structures can lead to variation in the dynamics, following a given amount of latent heating at fine grids, but not in our physics suites. The difference appears to be caused by the explicit interactions between gravity waves, occurring at fine grids only Groot et al. (2024) We assess whether their non-linear feedback from convective precipitation systems explains our joint PDFs of precipitation. The slightly exponential curve supports the interaction mechanism. These findings are further evidence for a non-linear feedback between convective organisation/aggregation and dynamics. This feedback has been studied earlier in a real-case study with ICON by looking from the fixed-physics rather than the fixed-dynamics perspective. Our current results may indicate that sub-grid physics with stochastic physics perturbations emulate convective organisation effects.

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:

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.