Abstract
Ocean heat content (OHC) anomalies typically persist for several months, making this variable a vital component of seasonal predictability in both the ocean and the atmosphere. However, the ability of seasonal forecasting systems to predict OHC remains largely untested. Here, we present a global assessment of OHC predictability in two state-of-the-art and fully-coupled seasonal forecasting systems. Overall, we find that dynamical systems make skilful seasonal predictions of OHC in the upper 300 m across a range of forecast start times, seasons and dynamical environments. Predictions of OHC are typically as skilful as predictions of sea surface temperature (SST), providing further proof that accurate representation of subsurface heat contributes to accurate surface predictions. We also compare dynamical systems to a simple anomaly persistence model to identify where dynamical systems provide added value over cheaper forecasts; this largely occurs in the equatorial regions and the tropics, and to a greater extent in the latter part of the forecast period. Regions where system performance is inadequate include the sub-polar regions and areas dominated by sharp fronts, which should be the focus of future improvements of climate forecasting systems.
Highlights
State-of-the-art seasonal forecast systems include a coupled ocean–atmosphere (Stockdale et al 1998, Baehr et al 2015; Batté et al 2019; Johnson et al 2019; MacLachlan et al 2015; Saha et al 2014; Sanna et al 2017, Takaya et al 2018) because the main source of seasonal predictability in many climate variables, on a global scale, is the quasiperiodic ocean–atmosphere interaction known as the El NiñoSouthern Oscillation (ENSO)
In CMCCSPS3, the initial conditions are based on C-GLORS (Storto & Masina 2016), while in ECMWF-SEAS5 they are based on ORAS5 (Zuo et al 2019)
This paper presents an assessment of the predictive skill of ocean heat content in the upper 300 m in two state-ofthe-art seasonal forecasting systems
Summary
State-of-the-art seasonal forecast systems include a coupled ocean–atmosphere (Stockdale et al 1998, Baehr et al 2015; Batté et al 2019; Johnson et al 2019; MacLachlan et al 2015; Saha et al 2014; Sanna et al 2017, Takaya et al 2018) because the main source of seasonal predictability in many climate variables, on a global scale, is the quasiperiodic ocean–atmosphere interaction known as the El Niño. The cycle of ENSO events, and the teleconnections, are strongly influenced by the subsurface ocean heat content (OHC) in the tropical Pacific (Doblas-Reyes et al 2013). Because of this crucial role in global predictability, the initialization of the subsurface thermal structure is key for successful seasonal predictions. The study of these products is very relevant for the generation of forecast systems To our knowledge, this is the first attempt to estimate the predictive skills of OHC at seasonal time scales and for the global ocean. We aim to provide a benchmark for future validation efforts, to explore dynamical reasons for measured forecast capability, and to highlight where forecast systems need improvement
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