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Rainfall is erratic and difficult to predict. Thus random models are often used to describe rainfall events. Since many of these random models are based more on statistics than physical laws, it is desirable to develop connections between the random statistical models and the underlying physics of rain. Here, a physics-based model is shown to converge to a statistics-based model, which helps to provide a physical basis for the statistics-based model.

This work is a theoretical contribution that provides equations for understanding uncertainty prediction applied in air quality where multiple chemical species can interact. A simplified minimal test-bed is introduced that shows the ability our equations to reproduce the statistics estimated from an ensemble of forecast. While the latter estimation is the state of the art, solving equations is numerically less costly, depending on the number of chemical species, and motivates this research.

The feasibility of physics-based forecasting of earthquakes depends on how well models can be calibrated to represent earthquake scenarios given uncertainties in both models and data. Our study investigates whether data assimilation can estimate current and future fault states in the presence of a bias in the friction parameter.

This paper is a follow up of a work by T. Miyoshi and Q. Sun which was published as NPG letters in 2022. The Control Simulation Experiment is applied to the Lorenz 96 model for avoiding extreme events. The results show that extreme events of this partially and imperfectly observed chaotic system can be avoided by applying pre-designed small perturbations. These investigations may be extended to more realistic numerical weather prediction models.

The impact of climate change on weather pattern dynamics over the North Atlantic is explored through the lens of information theory. These tools allow for clarifying the predictability of the succession of weather patterns, and the irreversible nature of the dynamics. It is shown that the predictability is increasing in the observations, while the opposite trend is found in model projections. The irreversibility displays an overall increase in time in both the observations and the model runs.

Atmospheric models often include random forcings, which aim to replicate the impact of processes too small to be resolved. Recent results in simple atmospheric models suggest that this random forcing can actually stabilise certain slow-varying aspects of the system, which could provide a path for resolving known errors in our models. We use randomly-forced simulations of a 'toy' chaotic system and theoretical arguments to explain why this strange effect occurs: at least in simple models.

Older observations of the Earth system sometimes lack information about the time they were taken, posing problems for analyses of past climate. To begin to ameliorate this problem, we propose new methods of varying complexity, including methods to estimate the distribution of the offsets between true and reported observation times. The most successful method accounts for the nonlinearity in the system, but even the less expensive ones can improve data assimilation in the presence of time error.

Climate science is, as all sciences, a social process, and as such conditioned by the Zeitgeist of the time. It has an old history, and has attained different political significances. Today, it is the challenge of anthropgenic climate change – and societies wants answers about how to deal with it. In earlier timers, it was mostly tthe ideology of climate determinism, which led people to construct superiority, and eventually colonialism.