@Article{GublerSBACEJMSSS:2020:AsECSE,
author = "Gubler, S. and Sedlmeier, K. and Bhend, J. and Avalos, G. and
Coelho, Caio Augusto dos Santos and Escajadillo, Y. and
Jacques-Coper, M. and Martinez, R. and Schwierz, C. and Skansi, M.
de and Spirig, C. H.",
affiliation = "Federal Office of Meteorology and Climatology, MeteoSwiss and
Federal Office of Meteorology and Climatology, MeteoSwiss and
Federal Office of Meteorology and Climatology, MeteoSwiss and
{Servicio Nacional de Meteorolog{\'{\i}}a e Hidrolog{\'{\i}}a
del Per{\'u}} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Servicio Nacional de Meteorolog{\'{\i}}a e
Hidrolog{\'{\i}}a del Per{\'u}} and {Universidad de
Concepci{\'o}n} and {Centro Internacional para la
Investigaci{\'o}n del Fen{\'o}meno de El Niņo} and Federal
Office of Meteorology and Climatology, MeteoSwiss and Servicio
Meteorol{\'o}gico Nacional, Buenos Aires and Federal Office of
Meteorology and Climatology, MeteoSwiss",
title = "Assessment of ECMWF SEAS5 seasonal forecast performance over South
America",
journal = "Weather and Forecasting",
year = "2020",
volume = "35",
number = "2",
pages = "561--584",
month = "Apr.",
abstract = "Seasonal predictions have a great socioeconomic potential if they
are reliable and skillful. In this study, we assess the prediction
performance of SEAS5, version 5 of the seasonal prediction system
of the European Centre for Medium-Range Weather Forecasts (ECMWF),
over South America against homogenized station data. For
temperature, we find the highest prediction performances in the
tropics during austral summer, where the probability that the
predictions correctly discriminate different observed outcomes is
70%. In regions lying to the east of the Andes, the predictions of
maximum and minimum temperature still exhibit considerable
performance, while farther to the south in Chile and Argentina the
temperature prediction performance is low. Generally, the
prediction performance of minimum temperature is slightly lower
than for maximum temperature. The prediction performance of
precipitation is generally lower and spatially and temporally more
variable than for temperature. The highest prediction performance
is observed at the coast and over the highlands of Colombia and
Ecuador, over the northeastern part of Brazil, and over an
isolated region to the north of Uruguay during DJF. In general,
Niņo-3.4 has a strong influence on both air temperature and
precipitation in the regions where ECMWF SEAS5 shows high
performance, in some regions through teleconnections (e.g., to the
north of Uruguay). However, we show that SEAS5 outperforms a
simple empirical prediction based on Niņo-3.4 in most regions
where the prediction performance of the dynamical model is high,
thereby supporting the potential benefit of using a dynamical
model instead of statistical relationships for predictions at the
seasonal scale.",
doi = "10.1175/WAF-D-19-0106.1",
url = "http://dx.doi.org/10.1175/WAF-D-19-0106.1",
issn = "0882-8156",
language = "en",
targetfile = "gubler_assessment.pdf",
urlaccessdate = "27 abr. 2024"
}