Emergent constraints are becoming popular for the identification of statistical relationships in observed current and past climate data that can be used to guide projections of future climate states. Their application is not without controversy, however, due to uncertainties about whether these relationships are indeed climate-invariant. This Opinion introduces an argument that Machine Learning tools can be useful for identifying climate-invariant relationships in historical data, especially those that are more complex, that can be expected to remain consistent under future climate scenarios.

Agriculture and food systems significantly affect atmospheric composition, air quality and ecosystems and their relative influence is growing due to the regulations of traditional industrial emissions and due to increasing food demand and changes in dietary choices. This opinion piece discusses not just the direct emissions of reactive nitrogen compounds, but also related pre- and post-stages, including fertilizer production, food processing and transport. The authors give valuable recommendations for future societally and environmentally responsible regulations of the food systems to meet global sustainability goals.