Assessment of climate change contribution to seasonal forecast anomalies

Numerical seasonal forecasting consists in predicting the expected distribution of several climate variables (e.g. temperature, precipitation) over the next three months, using a global climate model that is initialized with real-time observations. Seasonal forecasts are often communicated as anomalies with reference to the model climatology estimated from forecasts initialized over a past period (hindcasts).

These anomalies are affected by long term trends due to anthropogenic climate change. Consequently, most seasonal forecasts of temperature currently issued by the Copernicus Climate change services (C3S) in the last few years indicate warmer than normal conditions over Europe, regardless of the season. 

Here, we investigate three methods to quantify the contribution of climate change from seasonal forecasts of temperature anomalies, and compare it to the usual reference based on hindcast climatology. First, we use a linear trend fitted on hindcasts. This approach is usually used in the literature to evaluate the forecast skill as it provides an estimate of the  climate change response. However, this method relies on the major assumption that the anthropogenic climate (forced) response is linear, which is not always reasonable. The second method is based on a Bayesian technique which combines CMIP6 simulations and seasonal hindcasts to estimate the forced response within the model, assuming that it is indistinguishable from the CMIP6 ensemble. The third method is based on numerical seasonal forecast experiments initialized in a so-called counterfactual world unaffected by anthropogenic forcings: dynamical initial conditions are the same as for the real, factual, seasonal forecasts, but the thermodynamic initial conditions correspond to a colder climate representative of the hindcast climatology. From this protocol, the climate change contribution can be estimated from the difference between the factual and the counterfactual forecasts. In this work, the three methods are implemented on the operational Météo-France seasonal forecast. While both the Bayesian method and numerical experiments show consistent results in the forced response estimate, results from the linear method might be inappropriate or overly simplistic in some cases.