Summary
Seasonal forecasting is a field with enormous potential influence in different socio-economic sectors, such as water resources, agriculture, health, and energy. Yet, surface climate conditions in Europe still represent a hurdle to formulate skillful seasonal predictions. SD4SP aims to improve simulation and prediction of the remote influence of two dominant tropical variability modes in the North Atlantic-European (NAE) region: El Niño-Southern Oscillation (ENSO), and the Quasi-Biennial Oscillation (QBO); which are the leading modes of interannual variability in the tropical troposphere and stratosphere respectively. ENSO is highly predictable and constitutes the cornerstone of seasonal forecasting. The QBO is well constrained by initialization and has a long persistence, being considered as the most promising source of seasonal forecast quality with ENSO. However, many scientific questions remain unresolved concerning their tropical-extratropical teleconnections, and model systematic errors only worsen the problem. SD4SP will focus on the stratospheric pathway of the ENSO/QBO teleconnections to NAE and pursue gaining insight into the dynamical mechanisms at play. This goal will be undertaken by carrying out an unprecedented set of idealized seasonal forecast experiments to address the contribution of the tropical stratosphere and the polar stratosphere to the prediction skill by suppressing variability in the two stratospheric regions, separately. SD4SP is very timely in helping to reduce model biases and to increase current seasonal forecasting capabilities for the NAE surface climate. The goal of SD4SP is twofold: to identify key sources of predictability and to improve understanding and simulation of the mechanisms responsible for that predictability. SD4SP will bring together theory and applicability disentangling atmospheric teleconnections to satisfactorily exploit them in a seasonal prediction context.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101065820 |
| Start date: | 03-04-2023 |
| End date: | 02-04-2026 |
| Total budget - Public funding: | - 280 202,00 Euro |
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Original description
Seasonal forecasting is a field with enormous potential influence in different socio-economic sectors, such as water resources, agriculture, health, and energy. Yet, surface climate conditions in Europe still represent a hurdle to formulate skillful seasonal predictions. SD4SP aims to improve simulation and prediction of the remote influence of two dominant tropical variability modes in the North Atlantic-European (NAE) region: El Niño-Southern Oscillation (ENSO), and the Quasi-Biennial Oscillation (QBO); which are the leading modes of interannual variability in the tropical troposphere and stratosphere respectively. ENSO is highly predictable and constitutes the cornerstone of seasonal forecasting. The QBO is well constrained by initialization and has a long persistence, being considered as the most promising source of seasonal forecast quality with ENSO. However, many scientific questions remain unresolved concerning their tropical-extratropical teleconnections, and model systematic errors only worsen the problem. SD4SP will focus on the stratospheric pathway of the ENSO/QBO teleconnections to NAE and pursue gaining insight into the dynamical mechanisms at play. This goal will be undertaken by carrying out an unprecedented set of idealized seasonal forecast experiments to address the contribution of the tropical stratosphere and the polar stratosphere to the prediction skill by suppressing variability in the two stratospheric regions, separately. SD4SP is very timely in helping to reduce model biases and to increase current seasonal forecasting capabilities for the NAE surface climate. The goal of SD4SP is twofold: to identify key sources of predictability and to improve understanding and simulation of the mechanisms responsible for that predictability. SD4SP will bring together theory and applicability disentangling atmospheric teleconnections to satisfactorily exploit them in a seasonal prediction context.Status
SIGNEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
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