This study aims at understanding the impact of low-frequency climate modes on Rossby Wave Packets (RWPs) during the southern hemisphere summer. In particular, we focus on long-lived RWPs (lifespan above 8 days) and determine how El Niño-Southern Oscillation (ENSO) and Southern Annular Mode (SAM) modulate their frequency of occurrence plus the main areas of detection and dissipation. We find that the occurrence of long lived RWPs is maximum during El Niño years and negative SAM events. Years with largest numbers of long-lived RWPs are characterized by a zonally symmetric and narrow upper level jet that is shifted northward from its climatological position. Conversely, when the jet is shifted southward, as during positive SAM phases, particularly in the southwestern Pacific basin, the number of long-lived RWPs detected diminishes. El Niño sets atmospheric conditions that support the formation of long lived RWPs whereas La Niña years presents high interannual variability in the frequency of occurrence. Moreover, during El Niño events the main formation area is between 61-120ºE and its main dissipation area between 300-359ºE. During La Niña events, the main formation area moves to 241-300ºE and no main dissipation area is identified. During positive SAM two main formation areas appear at 61-120ºE and 241-300ºE and two main dissipation areas between 121-180º and 301-359º, whereas in negative SAM only one formation area at 241-300º is detected and no main dissipation area is found.

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.

The Madden-Julian Oscillation (MJO) is a major source of predictability on the sub-seasonal (10- to 90-days) time scale. An improved forecast of the MJO, may have important socioeconomic impacts due to the influence of MJO on both, tropical and extratropical weather extremes. Although in the last decades state-of-the-art climate models have proved their capability for forecasting the MJO exceeding the 5 weeks prediction skill, there is still room for improving the prediction. In this study we use Multiple Linear Regression and an Artificial Neural Network as post-processing methods to improve one of the currently best dynamical models developed by the European Centre for Medium-Range Weather Forecast (ECMWF). We show that the post-processing with the machine learning algorithm employed leads to an improvement of the MJO prediction. The largest improvement is in the prediction of the MJO geographical location and intensity.