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Climate change is one of the most critical challenges that our planet is facing today. Rising global temperatures are already bringing noticeable changes to Earth's weather and climate patterns with an increased frequency of unpredictable…
The atmospheres of hot Jupiters lie in a dynamical regime without a solar system analogue. The strongly irradiated daysides reach temperatures sufficiently hot for substantial thermal ionization of atmospheric species, resulting in flows…
Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated…
Latent heat flux is a primary pathway for ocean-atmosphere exchange of heat and moisture, yet the influence of sea surface temperature variability at fine scales ($\leq$ 100 km) on latent heat flux variability, particularly over the…
Mesoscale eddies produce lateral (2D) fluxes that need to be parameterized in eddy-permitting (1/4-degree) global ocean models due to insufficient horizontal resolution. Here, we systematically apply methods from the 3D LES community to…
Global climate projections rely on computationally demanding Earth System Models (ESMs), which are typically limited to coarse spatial resolutions due to their high cost. To obtain high-resolution projections for regions of interest, it is…
Machine learning (ML) has achieved remarkable success in climate and marine science. Given that greenhouse warming fundamentally reshapes ocean conditions such as stratification, circulation patterns and eddy activity, evaluating the…
A set of idealized experiments are performed to analyze the competing effects of declining atmospheric CO2 concentrations, the opening of an ocean gateway, and varying orbital parameters. These forcing mechanisms, which influence the global…
The present study investigates how global thermodynamic properties of the climate system are affected by the changes in the intensity of the imposed oceanic heat transport in an atmospheric general circulation model in aqua-planet…
We present a parameterization for the eddy diffusion profile of gas giant exoplanets based on physical phenomena and we explore how the parameterized eddy profile impacts the chemical composition, the thermal structure, the haze…
In situ and remotely sensed observations have potential to facilitate data-driven predictive models for oceanography. A suite of machine learning models, including regression, decision tree and deep learning approaches were developed to…
The under-representation of cloud formation is a long-standing bias associated with climate simulations. Parameterisation schemes are required to capture cloud processes within current climate models but have known biases. We overcome these…
We present a new statistical method to optimally link local weather extremes to large-scale atmospheric circulation structures. The method is illustrated using July-August daily mean temperature at 2m height (T2m) time-series over the…
A poleward-thinning ice shell can drive circulation in the subsurface oceans of icy moons by imposing a meridional temperature gradient--colder at the equator than the pole--through the freezing point suppression due to pressure. This…
This paper demonstrates the efficacy of data-driven localization mappings for assimilating satellite-like observations in a dynamical system of intermediate complexity. In particular, a sparse network of synthetic brightness temperature…
In a number of environmental studies, relationships between natural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the…
An optimisation scheme is developed to accurately represent the sub-grid scale forcing of a high dimensional chaotic ocean system. Using a simple parameterisation scheme, the velocity components of a 30km resolution shallow water ocean…
This study explores a physics-data driven hybrid approach for sea-ice column physics models, in which a machine learning (ML) component acts as a state-dependent parameterization of forecast errors. We examine how perturbations in snow…
We use a linear shallow-water model to investigate the global circulation of the atmospheres of tidally locked planets. Simulations, observations, and simple models show that if these planets are sufficiently rapidly rotating, their…
Inability of low-resolution ocean models to simulate many important aspects of the large-scale general circulation is a common problem. In the view of physics, the main reason for this failure are the missed dynamical effects of the…