Recent  

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.

This study investigates vertical localization based on a unique convection-permitting 1000-member ensemble simulation. We discuss requirements for vertical localization by deriving an empirical optimal localization (EOL) that minimizes the sampling error in 40-member sub-sample correlations assuming 1000-member correlations as truth. Results suggest that vertical localization should depend on different aspects that can help to improve vertical localization in future data assimilation systems.