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Abstract:

Floods are hydro-climatic extremes with severe socioeconomic and environmental consequences. Many studies have examined how large-scale modes of climate variability (e.g., ENSO, NAO) influence floods, but many have relied on catchments influenced by anthropogenic activities, which obscure underlying climate-flood relationships. Here, we use the newly released ROBIN Reference Hydrometric Network, a global dataset of over 3,000 near-natural catchments with daily streamflow records, to provide an observational assessment of climate-flood relationships at the global scale. We first quantify long-term and multi-temporal trends in annual flood peaks and peak-over-threshold events and evaluate their connections with key modes of climate variability across different IPCC regions. Trend analysis reveals how flood metrics have evolved across regions and time periods, while correlation analysis reveals the modes of climate variability that are associated with year-to-year variations in flood peaks and frequencies. A signal-to-noise framework tests whether global mean surface temperature leaves a detectable fingerprint on high flow regimes. This analysis helps to clarify the extent to which climate variability influences flood occurrence and magnitude in near-natural catchments worldwide. Moreover, we propose a machine learning-based process attribution framework to identify climate and catchment controls on floods in near-natural catchments. Preliminary results indicate substantial spatial variability in dominant flood drivers across and within IPCC regions and suggest that large-scale atmospheric circulation modes exert strong, but regionally distinct, influence on seasonal flood frequency. Overall, our findings underscore the importance of regional climate modes in modulating floods and provide the first global baseline on climate-driven changes to floods in near-natural catchments.

Abstract:

The mesoscale atmospheric energy spectrum has puzzled scientists for decades, sitting between classical turbulence and wave theories. Using year-long ECMWF operational analyses of high resolution and a spherical coarse-graining framework (Flowsieve), we present the first consistent global maps of local mesoscale kinetic energy fluxes. At 200~hPa, we identify a striking band of upscale transfer aligned with the ITCZ, while storm tracks and orography leave distinct dynamical imprints at both 200 and 600~hPa. By decomposing divergent and rotational components, we show that divergent energy dominates in the tropics and stratosphere, while rotational energy dominates in the extratropical troposphere. Conditioning spectra on this balance reveals contrasting regimes: a Nastrom–Gage-like spectrum under divergent dominance, and a spectrum reminiscent of the classical dual cascade of textbook two-dimensional turbulence under rotational dominance at 600~hPa. These results demonstrate that mesoscale energy transfer is shaped by a patchwork of mechanisms, reconciling long-standing debates and providing new inspiration for parametrisations and predictability in weather and climate models.

Abstract:

Machine learning hold the promise of unlocking more accurate and realistic parameterizations of atmospheric processes, but brings its own set of challenges and drawbacks. Among top issues are generalization, stability and interpretability. Here we present a parameter-efficient neural network parameterization which aims to address these issues by incorporating physical knowledge to a high degree. By predicting fluxes and microphysical process rates instead of total tendencies, the conservation of water can be hardcoded, which is shown to improve online performance. Furthermore, a physically motivated architecture based on vertically recurrent neural networks enables high computational efficiency and a low number of parameters. The models are trained and evaluated using a superparameterization setup with real orography. The impact of incorporating stochasticity is also discussed.

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 timescales. Here, we consider the separation of uncertainty by source using machine learning frameworks for subgrid-scale parameterisations. In this context, aleatoric uncertainty arises from internal variability in the training data, and epistemic uncertainty, arises from poorly constrained parameters during training. Using the Lorenz 1996 system as a testbed for simplified chaotic dynamics, we deal with uncertainties through a unified framework using Bayesian Neural Networks, to explore how the different sources of uncertainty evolve over different prediction timescales.

Abstract:

Marine plastic debris that is discarded into the ocean and eventually beaches is an acute problem for small island nations such as the Seychelles. To address this problem and enable anticipatory action, the sources of marine plastic debris need to be identified. Recent observations suggest that not all of the debris that arrives at the Seychelles is from terrestrial input. However, there is currently a lack of quantitative attribution of maritime debris sources.Therefore, we investigate the potential shipping and fishing vessel plastic debris sources in the southwestern Indian Ocean that beach at the Seychelles. We use a 2D Lagrangian particle-tracking model, based on OceanParcels, with particles that are advected by currents from a 1/50° (~2 km) regional ocean model. We combine this with satellite-tracked fishing and shipping data to inform particle starting locations and weightings. This model resolution allows us to resolve island to sub-island accumulation patterns.Spatially, model results suggest that debris beaching at the Seychelles from shipping vessels is concentrated along a limited number of high-activity shipping routes. The port destinations of these routes are consistent with the origin of plastic bottles inferred from labels in a previous study. Model results also show that 50-66% of fishing debris that beaches at the Seychelles is discarded within its own exclusive economic zone, depending on the island group.Temporally, the season during which debris is discarded strongly impacts the likelihood of beaching. This seasonal profile varies in amplitude and phase between islands due to wind-driven surface current changes. Additionally, we find debris beaching patterns can vary substantially between islands and on a sub-island scale. This highlights the importance of higher-resolution models for investigating plastic accumulation at kilometre-scale islands. We also assess the model against observations including bottle drifters and seasonal accumulation data at the Aldabra Atoll, the latter of which is consistent with sub-island scale accumulation seasonality from the model.Given that most marine debris originates from a few major shipping routes and from within the Seychelles exclusive economic zone, we suggest targeted enforcement of MARPOL Annex V could tackle the source of the issue.