Assessment of ECMWF SEAS5 Seasonal Forecast Performance over South America

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, Niño-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 Niño-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.

A combined view on precipitation and temperature climatology and trends in the southern Andes of Peru

“…The differences between the trends of maximum and minimum temperature, however, become smaller when removing the ENSO‐related part of the temperature time series. ENSO leads to larger maximum temperature, but has less impact on minimum temperatures (e.g., Gubler et al ., 2020). When removing the ENSO signal, the strongest reduction in trend magnitude is found for winter maximum temperature.…”

Techniques and skills of indigenous weather and seasonal climate forecast in Northern Ghana

“…Scientific advancements now make it possible to provide short and long-term climate information services to support farmers' decision-making (Gubler et al, 2020;Johnson et al, 2019;Mullen, 2007;Nyadzi et al, 2019;Scaife et al, 2019). Yet many farmers still use indigenous knowledge (IK) to adjust their farm practices or diversify their production to respond to local climate variability (Ebhuoma & Simatele, 2019;Eriksen et al, 2005;Radeny et al, 2019;Shoko & Shoko, 2012).…”

Verification of Weather and Seasonal Forecast Information Concerning the Peri-Urban Farmers’ Needs in the Lower Ganges Delta in Bangladesh

“…This can be attributed to the fact that Ratri's study used as the observational dataset a gridded dataset for the Southeast Asia-Observations (SA-OBS, [95]) which is based on a Kriging variogram. Additionally, Gubler et al [96] performed verification of SEAS5 over South America, and when comparing it with a previous study of Weisheimer and Palmer [44] that was verifying SEAS4, they concluded that higher reliabilities could be attributed to the improvement of the predictions due to spatial aggregation. However, improving performance by spatio-temporal aggregation limits their potential use only by large-scale farmers.…”