PNNL

Abstract

Agriculture is crucial for global food supply and dominates the Earth’s land surface. It is unknown, however, how slow but relentless changes in climate mean state, versus random extreme conditions arising from changing variability, will affect agroecosystems’ carbon fluxes, energy fluxes, and crop production. We used an advanced weather generator to partition changes in mean climate state versus variability for both temperature and precipitation, producing forcing data to drive factorial-design simulations of U.S. Midwest agricultural regions in the Energy Exascale Earth System Model. Here we show that an increase in temperature mean lowers stored carbon, plant productivity, and crop yield, can cause local to regional cooling, and can convert agroecosystems from a carbon sink to a source. The combined effect of mean and variability changes on carbon fluxes and pools was nonlinear, i.e. greater than each individual case. Overall, the scenario with change in both temperature and precipitation mean leads to the largest reduction in carbon fluxes (-16% gross primary production), carbon pools (-35% vegetation carbon), and crop yields (-33% and -22% median reduction in yield for corn and soybean, respectively). By unambiguously parsing the effects of changing climate mean versus variability, and quantifying their non-additive impacts, this study lays the foundation for more robust understanding and prediction of agroecosystems’ vulnerability to 21st-century climate change.