Laure Zanna Abstract:
Numerical simulations used for weather and climate predictions solve approximations of the governing laws of fluid motions on a grid. Ultimately, uncertainties in climate predictions originate from the poor representation of processes, such as ocean turbulence and clouds that are not resolved on the grid of global climate models. The representation of these unresolved processes has been a bottleneck in improving climate simulations.
The explosion of climate data and the power of machine learning algorithms are suddenly offering new opportunities: can we deepen our understanding of these unresolved processes and simultaneously improve their representation in climate models to reduce climate projections uncertainty?
Julien Brajard Abstract:
The ocean (including the sea-ice) is a major part of the climate system. Its main scale's variability is slower than other compartments (like the atmosphere), which makes it decisive to understand and predict the climate over decades. The ocean dynamics is represented by equations of fluid dynamics that can be solved in computer codes, called numerical models. While these equations are very similar to what is used in atmospheric science, the ocean dynamics is specific by the aforementioned difference in temporal scales. Another specificity of ocean studies is the availability of observations that are relatively recent, and very imbalanced toward the surface of the ocean, especially since the satellite ages (end of the 1970s). As a consequence, ocean and sea-ice models still contain large biases and unknown and several essential climate variables, as defined by the Global Climate Observing System (GCOS), are highly uncertain. Following approaches used in the numerical weather forecast community, this gap of knowledge can be addressed by combining the observations with the numerical models using, for instance, data assimilation techniques.