Seasonal weather/climate forecast systems provide indications to unusual climate conditions in the atmosphere, ocean, land and other components of the climate. These systems are capable of predicting climate variables such as temperature and precipitation months in advance.
A main reason for this capability is the famous ocean–atmosphere interaction known as the El Nino Southern Oscillation(ENSO), which is responsible for the El Nino/La Nina phenomena. ENSO alters the atmospheric circulation across the entire tropical Pacific and causes teleconnections which change seasonal climate across the world.
The tropical Pacific is currently in a La Nina state (alter ego of El Nino) in line with the pattern of the seasonal seesawing sea-surface temperatures, but this is forecast to transition into ‘neutral’ (neither El Nino or La Nina) conditions during March-April-May and into the first half of the South-West monsoon in India.
‘Memory bank’ on top
The upper ocean acts as a “memory bank” by providing long-term heat storage for the region. The ability to predict seasonal changes is therefore strongly influenced by the tropical Pacific.
Ocean heat content 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 ocean heat content remains largely untested.
A study just published on Climate Dynamics led by the CMCC Foundation, the Euro-Mediterranean Centre on Climate Change (CMCC), presents an assessment of the predictive skill of ‘ocean heat content’ in the upper 300 m in seasonal forecasting systems.
Ocean heat content
“There was no extensive validation of ocean heat content in seasonal forecasting systems, despite its important role in seasonal predictability and the potential applications”, explains Ronan McAdam, CMCC researcher at Ocean Modelling and Data Assimilation Division and first author of the study. “To our knowledge, this is the first attempt to estimate the predictive skills of the heat content at seasonal time scales and for the global ocean.”
Overall, researchers found out that dynamical systems make skillful seasonal predictions of the heat content in the upper 300 m across a range of forecast start times, seasons and dynamical environments. The upper 300 m was chosen because it encompasses many diverse phenomena across the ocean which are either relevant for predictability or applications.
Sub-surface heat trends
In the tropics, the cycle of ENSO and the events correlated to this phenomenon are strongly influenced by the sub-surface ocean heat content in the tropical Pacific, while in the North Atlantic, ocean heat content anomalies affect the formation of hurricanes. Marine wildlife is also affected by habitat displacement and shrinking occurring below the surface. Thus, early prediction of these anomalies may aid mitigation of extreme events.
The study shows that there is potential to make accurate predictions of sub-surface warming up to two seasons in advance, opening up a wide range of potential applications of marine seasonal forecasting. For example, seasonal lead times would provide an early prediction of ocean conditions which render extreme heat events more likely, and therefore provide fisheries, aquaculture farms and marine protected areas ample time to prepare for adverse events.
Marine heat waves
McAdam notes that an exciting and urgent task for seasonal forecasting in the near future is the prediction of marine heat waves, which either occur at depth or are driven by subsurface heat anomalies. The role of ocean heat content in marine heatwaves is in fact twofold: increased heat content can make the heatwaves more likely to occur and can therefore be a driver of an ocean-driven heatwave, or can be itself an indication that a heatwave is happening.
The average duration of such events is increasing globally, more so in the Indian Ocean, and is crossing into the timescales of seasonal forecasts. Fortunately, McAdam observes, events driven by subsurface warming are expected to be more predictable than those primarily driven by relatively abrupt atmospheric disturbances. Early prediction of subsurface heating could be of great economic and practical benefit to several industries such as aquaculture and fishing, and could aid marine conservation efforts against mass-mortality events, says McAdam.
CMCC, ECMWF systems
The two forecast systems used in this research are the Seasonal Prediction System Version 3 from the CMCC Foundation (CMCC-SPS3), and the fifth generation Seasonal Forecasting System from the European Centre for Medium-Range Weather Forecasts (ECMWF-SEAS5).
Since 2018, both systems have been contributing to the Copernicus Climate Change Service (C3S), which makes seasonal forecasts of precipitation, 2 m-temperature, and is available for free online.