Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

Abstract

Nowadays, a challenge in Climate Science is the seasonal forecast and knowledge of the model’s performance in different regions. The challenge in South America reflects its huge territory; some models present a good performance, and others do not. Nevertheless, reliable seasonal climate forecasts can benefit numerous decision-making processes related to agriculture, energy generation, and extreme events mitigation. Thus, given the few works assessing the ECMWF-SEAS5 performance in South America, this study investigated the quality of its seasonal temperature and precipitation predictions over the continent. For this purpose, predictions from all members of the hindcasts (1993–2016) and forecasts (2017–2021) ensemble were used, considering the four yearly seasons. The analyses included seasonal mean fields, bias correction, anomaly correlations, statistical indicators, and seasonality index. The best system’s performance occurred in regions strongly influenced by teleconnection effects, such as northern South America and northeastern Brazil, in which ECMWF-SEAS5 even reproduced the extreme precipitation anomalies that happened in recent decades. Moreover, the system indicated a moderate capability of seasonal predictions in medium and low predictability regions. In summary, the results show that ECMWF-SEAS5 climate forecasts are potentially helpful and should be considered to plan various strategic activities better.

Catchment‐scale skill assessment of seasonal precipitation forecasts across South Korea

“…Because of these systematic biases, forecast skill is generally improved by linear bias correction, as also found in previous studies (e.g., Charles et al, 2013;Crochemore et al, 2016;Ferreira et al, 2022). The exception is the wet season of dry years, where bias correction could be detrimental because it increases the forecasted precipitation away from the observations (see Figure 9).…”

“…Although SPFs provide skilful forecast over the climatology during the wet season of dry years, we found that the limitation lies in anticipating dry years. A previous study also investigated to show if SPFs from ECMWF have the potential to predict extreme dry conditions over South America (Ferreira et al, 2022), and found that this potential is shown mainly in the tropical sectors having strong connection with ENSO (e.g., Weisheimer & Palmer, 2014; Shirvani & Landman, 2015 for Iran). Our result also shows that both SPFs and ENSO failed to capture a reliable signal of a dry year in South Korea.…”

“…Lastly, there have also been many efforts to connect forecast skill to large‐scale climate patterns such as El Niño–Southern Oscillation (ENSO). It has been demonstrated that forecast skill deteriorates when moving away from the El Niño region (e.g., Weisheimer & Palmer, 2014; Shirvani & Landman, 2015 for Iran; Ferreira et al, 2022 for South America).…”

Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America

“…However, even with a wetter simulation pattern, these models underestimate rainfall The ensemble mean maintains the warmer behavior of the models in sectors such as the Andean region and north-central Argentina, as well as the colder pattern in extreme southern Argentina and northern SA. Other studies have also found a colder bias of climate models in SA [27,[102][103][104][105][106] due to the limitation of the models in simulating the complex topography of the continent, especially over regions such as the Andes and the La Plata Basin [107], as well as the initial soil moisture conditions since this variable modifies the surface temperature amplitude [108].…”

Literature survey of subseasonal‐to‐seasonal predictions in the southern hemisphere

“…Enhancing forecast skill begins with understanding the sources and limits of S2S predictability within the Earth system. Previous studies revealed that there are potential sources of predictability for the S2S timescales, including the MJO, the state of ENSO, soil moisture, snow cover and sea ice, stratosphere–troposphere interaction, and tropical–extratropical teleconnections (e.g., Ferreira et al, 2022; Ichikawa & Inatsu, 2017; Kim et al, 2014; Mariotti et al, 2020; Wang et al, 2016). The sources of S2S predictability are each briefly discussed as follows:…”