Improving the predictive ability of global carbon cycle models

Carbon management would be much more effective to mitigate climate change when Earth system models (ESMs) provide reliable predictions of the response of the carbon cycle to different management practices. However, the current generation of ESMs generates diverse projections, encouraging skepticism of scientific results and undermining effective policy design. ESMs can be substantially improved with the growing and diverse sets of observations to constrain model structures, parameterization, and forcings. Model skills for short-term prediction can be improved through coordinated observation and modeling during measurement campaigns. Model predictive ability will be gained over time via the co-evolution of coupled observation-prediction systems