Climate Change Contribution to Seasonal Forecast Anomalies Using Constrained CMIP6 Projections

Année de publication

2026

The climate anomalies predicted by real-time seasonal forecasts are constructed with respect to a hindcast climatology over a past period. With climate change, the climatology no longer reflects current climate conditions, systematically biasing predictions toward warm anomalies and complicating their operational use. Therefore, there is a need to properly quantify the signal in seasonal forecasts that arises from long-term climate change. This study introduces a new method to estimate this climate change contribution, as an alternative to the commonly used linear trend on the hindcast period. This method is based on a Bayesian technique called Kriging for Climate Change (KCC) and combines CMIP6 simulations and initialized seasonal hindcasts to estimate the forced response within the model. We apply this method to 2 m-temperature real-time seasonal forecasts from Météo-France and compare it to the linear trend benchmark, with a focus on the June-July-August (JJA) and December-January-February (DJF) seasons, initialized on 1 May and 1 November respectively. Results show that both methods provide similar global patterns but differ in the intensity of the estimated climate change contribution, especially over mid-latitudes. We find that the KCC method is more robust than the linear trend because it is not wrongly influenced by strong events that are independent from climate change, such as strong El Niño events. The KCC method also provides an uncertainty associated with the climate change contribution. We take advantage of this uncertainty to assess the role of internal variability in seasonal forecast and revisit the system skill in predicting it. This new method provides seasonal forecasters with a robust way to properly decompose temperature anomalies into two components, one representing the climate change signal and one reflecting internal variability. It also offers an additional scientific basis to better understand the role of climate change within seasonal forecasting systems.</div>

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