Abstract 128

Abstract ID: 128

Evaluating the representation of South America precipitation variability patterns
in sub-seasonal predictions of S2S project models

Lead Author: Felipe M. de Andrade
National Institute for Space Research, Brazil

Keywords: Sub-seasonal precipitation, Prediction quality, Potential predictability, Predictability sources, South America

Abstract: Anticipated sub-seasonal precipitation predictions (expected conditions for next 4 weeks) are important for activity planning in various application sectors, such as water management, agriculture, and energy production. South American sub-seasonal precipitation is strongly influenced by key modes of variability, with well-defined spatial patterns, representing the main sources of predictability in this time scale. Therefore, it is important to investigate how well sub-seasonal prediction models represent these precipitation patterns to have an assessment of their reproducibility and to better understand the mechanisms behind the actual prediction quality. To achieve this goal, Empirical Orthogonal Function analysis is used to identify these spatial patterns, together with the corresponding principal component time series, in observations and a selection of Sub-seasonal to Seasonal (S2S) Prediction Project models, including the multi-model ensemble mean of these models. The employed methodology also allows quantifying the contribution of the identified patterns to the actual S2S models precipitation retrospective prediction quality, assessed through the association attribute, as well as to the estimated potential predictability. The Madden-Julian Oscillation and the El NiƱo-Southern Oscillation were identified as the main climate drivers contributing to both retrospective austral summer sub-seasonal precipitation prediction quality and the estimated potential predictability over South America.

Caio Coelho (National Institute for Space Research – Brazil)
Marisol Osman (Karlsruhe Institute of Technology – Germany)
Mariano Alvarez (University of Buenos Aires – Argentina)
Carolina Vera (University of Buenos Aires – Argentina)
Iracema Cavalcanti (National Institute for Space Research – Brazil)