The ensemble mean and standard deviation y(X n ) and s(X n ) can further be used to estimate the contributions of bias and variance error to the overall observed MAE via Bayesian inference. Here, we follow the method of Ref. 60. In this approach, the different predictions made by individual models i for a single test datapoint, ŷi,j (X n ), are assumed to be normally distributed around a mean distribution value µ j (X n ), with a spread related to the ensemble standard deviation s(X n ). In accordance to the central limit theorem, an ensemble prediction y(X n ) will converge to µ j (X n ) at very large ensemble sizes. The nonvariance contribution to error is considered to be the absolute error occurring in a theoretical very large ensemble