Using pseudo-global-warming storylines and process-based ecosystem models for impact assessment of extreme events on agricultural production.

With an increasing probability of extreme events, their significance for agricultural production has also grown. Ecosystem models enable us to integrate current knowledge about plant-climate interactions with climate change scenarios. Since impacts of weather extremes differ depending on crop, intensity, length, and timing, a process-based approach is necessary to quantify to what extent extreme events impact agricultural production. We used the ecosystem model LandscapeDNDC to evaluate the effect of extreme conditions, like drought or intense heat waves, on agricultural production. We modified LandscapeDNDC to better account for heat stress by integrating a yield reduction function dependent on the timing and intensity of a heat wave and crop fatality due to stress overload. We validated the model performance using historical yield records at the regional scale.

In the second step, we applied pseudo-global-warming storylines to assess how the extreme heat wave of 2018 – 2022 would have affected yields of maize and wheat in a + 2 K warmer world. This exercise identifies which regions are most vulnerable regarding climate extremes and quantifies to what extent extreme climate events can affect crop yields compared to baseline conditions. Using process-based ecosystem models in combination with storyline-based climate projections is a promising approach to assess the impact of low-probability extreme weather events with a potentially high-impact outcome on agricultural production.