From Germany to Brazil: on climate risk communication

by Javier García-Pintado

Last week, on 22-23 October 2018, around 230 scientists from the three ocean and climate related clusters of excellence in northern Germany met in Berlin in the joint conference on Ocean – Climate – Sustainability Research Frontiers. The participants brought in lively discussions within the context of scientific and societal action towards ocean and climate research. Apart from the discussions more oriented toward the basic climate science and technical aspects, from a personal standpoint (perhaps because of its distance from my own work), I found a number of presentations from “The Future Ocean” cluster in Kiel, which include scholars from politics, social science, philosophy and international law most interesting. Some of these presentations offered a window on the connection between climate change and global and local politics in countries (e.g.; as tropical islands in the Indian ocean, who generally rely on external aid) most affected by increasing sea levels and coastal erosion. In common, this class of talks indicated a need for improving the communication of climate and natural risk science to society. Actually, a huge component of the unpredictability in future climate projections comes from the societal component.

However, as analyzed in one talk in the conference, it seems that, ultimately, public opinion is mostly driven by what is shown on TV, and TV, public offer is in turn mostly driven by the economic powers. Thus, as described the writer Jose Luis Sampedro more than 6 years ago, “public opinion” (defined in Wikipedia as consisting of the “desires, wants, and thinking of the majority of the people”), is in reality the “opinion of the media” or the “opinion of the economic powers”. This clearly connects to the results of Brazil elections just yesterday and the new presidency, and so to the derived very uncertain future of the Amazon management. Apart from the risks to biodiversity, a further deforestation of the Amazon rainforest would make it impossible to cut carbon pollution and the aspirational target of no more than 1.5ºC global warming above pre-industrial temperatures set in the Paris climate agreement. Brazilian people (and they are not alone) seem either oblivious to the problem or convinced that they are not affected by it (even, as from a friend’s personal communication last week, it appears that some people in Brazil sadly believe climate change is an European hoax to take control over their rainforest). Generally rising sea levels and increased storm surge risks, as well as the extra energy accumulated in the Earth system in general (and ocean in particular, boosting atmospheric convection and associated flood risks), will surely lead to a further demand of online, continuously updated, risk information to face emergency situations in the future city. One can wish the best for Brazil and the Amazon, which is the best for the world. In any case, let’s hope that Copacabana is not swallowed in the sea before Rio is transformed into a resilient city.

Machine learning and data assimilation

Machine learning and data assimilation

by Rossella Arcucci

Imagine a world where it is possible to accurately predict the weather, climate, storms, tsunami and other computational intensive problems in real time from your laptop or even mobile phone – if one has access to a supercomputer then to be able to predict at unprecedented scales/detail. This is the long term aim of our work on Data Assimilation with Machine Learning at the Data Science Institute (Imperial College London, UK) and as such, we believe, it will be a key component of future Numerical Forecasting systems.

We proved that the integration of machine learning with Data assimilation can increase the reliability of prediction, reducing errors by including information with an actual physical meaning from observed data. The resulting cohesion of machine learning and data assimilation is then blended in a future generation of fast and more accurate predictive models. This integration is based on the idea of using machine learning to learn the past experiences of an  assimilation process. This follows the principle of Bayesian approach.

Edward Norton Lorenz stated “small causes can have larger effects”, the so called butterfly effect. Imagine a world where it is possible to catch “small causes” in real time and predict effects in real time as well. To know to act! A world where science works with continuously learning from observation.

Figure 1. Comparison of the Lorenz system trajectories obtained by the use of Data Assimilation (DA) and by the integration of machine learning with Data assimilation (DA+NN)