S<sup>4</sup>CAST v2.0: sea surface temperature based statistical seasonal forecast model

Abstract

Abstract. Sea Surface Temperature is the key variable when tackling seasonal to decadal climate forecast. Dynamical models are unable to properly reproduce tropical climate variability, introducing biases that prevent a skillful predictability. Statistical methodologies emerge as an alternative to improve the predictability and reduce these biases. Recent studies have put forward the non-stationary behavior of the teleconnections between tropical oceans, showing how the same tropical mode has different impacts depending on the considered sequence of decades. To improve the predictability, the Sea Surface Temperature based Statistical Seasonal foreCAST model (S4CAST) introduces the novelty of considering the non-stationary links between the predictor and predictand fields. This paper describes the development of S4CAST model whose operation is focused on the study of the predictability of any variable related to sea surface temperature. An application focused on West African rainfall predictability has been implemented as a benchmark example.

Potential SST drivers for Chlorophyll-a variability in the Alboran Sea: A source for seasonal predictability?

“…The predictive skill at seasonal time scales has been assessed using cross-validated hindcasts based on the maximum covariance analysis (MCA) methodology (Bretherton et al, 1992;Suaŕez-Moreno and Rodrıǵuez-Fonseca, 2015). MCA performs a singular value decomposition of the covariance matrix compounded by a predictor and a predictand field in such a way that the covariance of the associated expansion coefficients (timeseries) of the resultant modes is maximized.…”

Statistical-Observational Analysis of Skillful Oceanic Predictors of Heavy Daily Precipitation Events in the Sahel

“…In this paper, the novel Sea Surface temperature based Statistical Seasonal ForeCAST model (S4CAST; [36]) was utilized to exhaustively analyze the timing and spatial structure of the relation between Sahel heavy/extreme (75/95th percentile) daily precipitation events and skillful oceanic predictors identified in previous work: El Niño Southern Oscillation (ENSO) and the Mediterranean Sea [37]. To this aim, the NOAA Extended Reconstructed Sea Surface Temperature (SST) data set (ERSST), the Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) and the European Center for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis were utilized for the period of 1982-2015.…”

“…Therefore, the lower its value, the better approach, whereas high values of RMSE indicate that the forecasts overestimate or underestimate the amplitude of the observations. Further information on the S4CAST can be found in Suárez-Moreno [36].…”