Research
Climate variability in the Southern Hemisphere and South America
Climate variability on Interannual timescales in the Southern Hemisphere, particularly South America, is influenced by various climate patterns. The El Niño-Southern Oscillation (ENSO) plays a significant role, causing shifts in rainfall and temperature anomalies. Additionally, the Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD) contribute to the complexity of this variability, affecting atmospheric circulation and sea surface temperatures. Understanding these interactions is crucial for predicting and managing the impacts on agriculture, water resources, and ecosystems in the region.
In my research I focus on understanding the combined influence of these patterns on the climate of South America, using either reanalysis as well as model simulations and initialized predictions.
Subseasonal Preditability of North Atlantic-European Weather Regimes and its applications in the energy sector
Weather regimes are quasi-stationary, recurring and persistent large-scale patterns which modulates the surface weather variability on daily to weekly timescales. Weather regimes can be a useful predictor in a range of applications, in particular, the energy sector. Understanding the predictability of weather regimes on subseasonal timescales can help anticipate the evolution of weather for days up to weeks ahead.
In my research I focused on the year-round North Atlantic-European weather regimes defined by Grams and co-authors to address the following questions: 1) Can forecast systems skillfuly forecast weather regimes weeks ahead. 2) What are the difference in performance between forecast systems? 3) What are the sources of errors in the weather regime forecasts?
I did this research in collaboration with Dr Christian Grams from KIT and Dr Remo Beerli from AXPO solutions.
We have published a summary of the multi-model assessment of the predictive skill of S2S models in this article.
Subseasonal and Seasonal prediction in South America
The development of regional climate services requires understanding the predictability and prediction skill of climate variability on different timescales. Sub-seasonal and Seasonal prediction ($\sim$10-30 days) has been a topic of great interest during the last decades, due to the need of multiple socioeconomic sectors for extended-range forecasts as skillful as those for the medium-range forecast horizon. Atmospheric predictability on subseasonal timescales is related to both the initial conditions and to the slowly evolving climate variations in the Earth System, such as the ocean or the sea-ice. On seasonal timescales, the variations in Earth System components are the dominant source of skill.
During my PhD I studied the predictability of climate in South America on Seasonal timescales. In addition, I developed a calibrated and consolidated probabilistic seasonal forecasts for South America that is now implememted in the Argentinian Weather Service. I have also supervised students working on documenting the predictability on seasonal timescales by different coupled climate models.
In recent years, I have also studied the predictability of heat and cold waves on subseasonal timescales and I have collaborated in different studies addressing the predictability on subseasonal timescales.