This paper describes LFRic: the new weather and climate modelling system being developed by the UK Met Office to replace the existing Unified Model in preparation for exascale computing in the 2020s. LFRic uses the GungHo dynamical core and runs on a semi-structured cubed-sphere mesh. The design of the supporting infrastructure follows object-oriented principles to facilitate modularity and the use of external libraries where possible. In particular, a 'separation of concerns' between the science code and parallel code is imposed to promote performance portability. An application called PSyclone, developed at the STFC Hartree centre, can generate the parallel code enabling deployment of a single source science code onto different machine architectures. This paper provides an overview of the scientific requirement, the design of the software infrastructure, and examples of PSyclone usage. Preliminary performance results show strong scaling and an indication that hybrid MPI/OpenMP performs better than pure MPI.

The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford workshop on Machine Learning for Weather and Climate, this paper also presents thoughts on medium-term goals to advance such use of AI, which include assuring that algorithms are trustworthy and interpretable, adherence to FAIR data practices to promote usability, and development of techniques that leverage our physical knowledge of the atmosphere. The coauthors propose several actionable items and have initiated one of those: a repository for datasets from various real weather and climate problems that can be addressed using AI. Five such datasets are presented and permanently archived, together with Jupyter notebooks to process them and assess the results in comparison with a baseline technique. The coauthors invite the readers to test their own algorithms in comparison with the baseline and to archive their results. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

How exactly our brain works is still an open question, but one thing seems to be clear: biological neural systems are computationally powerful, robust and noisy. Using the Reservoir Computing paradigm based on Spiking Neural Networks, also known as Liquid State Machines, we present results from a novel approach where diverse and noisy parallel reservoirs, totalling 3,000 modelled neurons, work together receiving the same averaged feedback. Inspired by the ideas of action learning and embodiment we use the safe and flexible industrial robot BAXTER in our experiments. The robot was taught to draw three different 2D shapes on top of a desk using a total of four joints. Together with the parallel approach, the same basic system was implemented in a serial way to compare it with our new method. The results show our parallel approach enables BAXTER to produce the trajectories to draw the learned shapes more accurately than the traditional serial one. Index Terms-spiking neural networks, liquid state machines, reservoir computing, humanoid robots, BAXTER, V-REP, parallel processing.

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Teleconnections are sources of predictability for regional weather and climate but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data is usually unclear. Here we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections.