the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Explaining the high skill of reservoir computing methods in El Niño prediction
Francesco Guardamagna
Claudia Wieners
Henk A. Dijkstra
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The mid-Pliocene, a geological period around 3 million years ago, is sometimes considered the best analogue for near-future climate. It saw similar CO2 concentrations to the present-day but also a slightly different geography. In this study, we use climate model simulations and find that the Northern Hemisphere winter responds very differently to increased CO2 or to the mid-Pliocene geography. Our results weaken the potential of the mid-Pliocene as a future climate analogue.
emergent constraintsuses observations of current climate to improve those predictions, using relationships between different climate models. Our paper first classifies the different uses of the technique, and continues with proposing a mathematical justification for their use. We also highlight when the application of emergent constraints might give biased predictions.
too lateto start reducing GHGs in order to avoid dangerous anthropogenic interference. We develop a method for determining a so-called point of no return (PNR) for several GHG emission scenarios. The innovative element in this approach is the applicability to high-dimensional climate models.
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Using an operational numerical weather prediction framework, our numerical results show that TCI makes the system accurately generate new reflectivity cells and significantly improves the fractional skill score of forecasts over lead times of up to six hours by up to 10 %.
naturein a computational simulation. Idealized experiments with a low-order chaotic system show successful results by small control signals of only 3 % of the observation error. This is the first step toward realistic weather simulations.
trained– and then can be used to predict the evolution in the future. We show some limitations in this approach for certain systems that are important to consider when using neural networks for climate- and weather-related applications.