SEAS5: The new ECMWF seasonal forecast system

Abstract

Abstract. In this paper we describe SEAS5, ECMWF’s fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill. An important improvement in SEAS5 is the reduction of the Equatorial Pacific cold tongue bias, which is accompanied by a more realistic ENSO amplitude and an improvement in ENSO prediction skill over the central-west Pacific. Improvements in two-metre temperature skill are also clear over the tropical Pacific. SST biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF two-metre temperature prediction skill in this region. The prognostic sea ice model improves seasonal predictions of sea ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in two-metre temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in summer. In summary, development and added complexity since System 4 has ensured SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in ENSO prediction.

Does increased atmospheric resolution improve seasonal climate predictions?

“…However, the benefits of increased atmospheric resolution for seasonal prediction skill are less clear. Operational seasonal prediction systems are now being at resolutions well beyond one degree (e.g., MacLachlan et al, 2014;Johnson et al, 2018). The effects on prediction skill from further increases in resolution in the atmosphere (e.g., Jung et al, 2012;Jia et al, 2015;Zhu et al, 2015;Prodhomme et al, 2016) or ocean (e.g., Kirtman et al, 2017) is an active research topic and it is not known whether this outweighs the benefits of larger ensemble size (e.g., Scaife et al, 2014;Doi et al, 2019), increased vertical resolution (e.g., Marshall and Scaife, 2010;Butler et al, 2016) or improved initial conditions (e.g., Kumar et al, 2015;Nie et al, 2019).…”

When and where do ECMWF seasonal forecast systems exhibit anomalously low signal‐to‐noise ratio?

“…Details of SEAS5's configuration and performance, including stratospheric biases, are documented in Johnson et al . ().…”

A seamless filter for daily to seasonal forecasts, with applications to Iran and Brazil

“…According to Johnson et al . (), fig. 20, both regions lie at the boundary of skilful tropical predictions (DJF, 1‐month lead).…”

“…Originally used for the interface between weather and climate (Palmer et al ., ; Shukla et al ., ), the word “seamless” now refers to efforts of unifying prediction systems (initialization, parametrization, numerics) across all time‐scales. This study emphasizes predictions ranging from days to seasons, specifically from lead times of 1 to 180 days, as produced by the latest seasonal prediction system SEAS5 of the European Centre of Medium‐range Weather Forecasts (ECMWF) (Johnson et al ., ). Potential atmospheric predictability at seasonal time‐scales is grounded in, mostly coupled, atmosphere–ocean processes, such as the El‐Niño–Southern Oscillation or the Madden–Julian Oscillation, that are themselves predictable at those time‐scales (Rowell, ).…”