Feeling the heat: how the AIFS learns the urban heat island

Summary

The summer of 2026 has been defined so far by unprecedented levels of heat. Since late May, a rapid succession of heatwaves has moved across Europe. June was the hottest on record for western Europe, some three degrees above the 1991–2020 average. England and France both recorded their hottest June ever, while several countries broke all-time records, and more than 10,000 excess deaths were reported across the continent in the final week of June alone. A further heatwave followed in early July. Heat of this kind is felt most acutely in cities. Dense built-up surfaces, reduced vegetation, and trapped heat from urban infrastructure contribute to the urban heat island effect, causing urban areas to remain warmer than their rural surroundings. The difference is often most pronounced at night, when cities release stored heat more slowly. During heatwaves, persistently high overnight temperatures can substantially increase health risks by increasing cumulative heat exposure and reducing opportunities for recovery. For a forecasting system to capture the urban heat island effect, it needs to know where the cities are. As such, the next version of the Artificial Intelligence Forecasting System (AIFS v3) is set to include a static field representing urban extents as forcing; here we show what the machine learning (ML) model does with that field. Giving the AIFS a view of the city The AIFS has been steadily growing beyond its initial core atmospheric remit, using Anemoi to represent more of the Earth system with machine learning. Static fields – fixed maps that give the model information about the environment in which weather develops – are an important part of that expansion. The urban field is one such addition. It tells the model, at every grid point, the impervious fraction of the surface – the proportion covered by roads and buildings – giving a static description of the urban environment that complements the atmospheric state. Isolating the urban signal To see what the model attributes to the urban field, we first run the AIFS prototype as normal, forced with the standard urban field, then repeat the forecast with the urban field set to zero everywhere. This effectively removes cities from the model's view, while keeping the initial conditions identical. The difference between the two forecasts isolates the model's learned urban response, separating it from the…