Abstract
Climate and weather extremes such as heat waves, droughts, extreme
precipitation events or cold surges have huge social and economic
impacts that are expected to increase with climate change. Forecasting
of such extreme events on the sub-seasonal time scale (from 10 days to
about 3 months) is very challenging because of the poor understanding of
phenomena that may increase predictability at this time scale. The
Madden-Julian Oscillation (MJO) is the dominant mode of variability in
the tropical atmosphere on sub-seasonal time scales and can also promote
or enhance phenomena such as monsoons and hurricanes in other regions of
the world. Here we develop artificial neural networks that can lead to a
very competitive MJO prediction. While our average prediction skill is
about 26-27 days (which competes with that obtained with computationally
demanding state-of-the-art climate models), for some initial phases the
methodology has a prediction skill of 60 days or longer.