On the robustness of AI model forecast skill and initial condition uncertainty of the 2021 Pacific Northwest Heatwave

AI weather models are becoming valuable tools for predicting the weather. While AI models’ general forecasts are known to be skillful, their forecast skill of extreme events is not fully understood. The 2021 Pacific Northwest (PNW) heatwave is a good case study for AI models because it falls outside of the distribution of heat waves in AI model training datasets.

Here, we investigate the forecast performance of 8 AI models (AIFS, Gencast, NeuralGCM, Graphcast, Fuxi, Pangu, Fourcastnet, FourcastnetV2) of the 2021 PNW heatwave. Despite the event being out of the training dataset distribution, their forecast performance is comparable to that of a state-of-the-art numerical weather prediction model (IFS). Specifically, AI models and IFS can accurately forecast the heatwave for lead times less than 7 days.

Two recent studies suggest the predictability barrier of the PNW heatwave may be due to an initial condition observation error. Leach et al. (2024) found that the 26th ensemble member of a 250 member IFS forecast accurately forecasts the heatwave 12 days in advance. Vonich and Hakim (2024) used backpropagation in Graphcast to find an optimal initial condition that leads to an accurate forecast 10 days in advance. Are these initial conditions robust across an ensemble of AI models? And do these initial conditions point to a unique solution?

We find a large spread in forecast accuracy when running the 8 AI models with the Leach et al. (2024) and Vonich & Hakim (2024) initial conditions. Furthermore we ran 1000 member ensembles in NeuralGCM and find initial conditions that lead to an accurate long-term forecast are not unique. These results suggest that the improvement in forecast accuracty to certain initial conditions may not necessarily be due to the initial conditions being closer to ground truth but rather they are due to cancelation of model error.