Atmospheric CO2 observations for prediction of carbon cycle feedbacks
Carbon cycle feedbacks are one of the most uncertain components of global climate predictions. In the long term, carbon fluxes will respond to increased anthropogenic CO2 concentrations and associated changes in temperature and other climate metrics. In this talk, I will discuss carbon cycle models that range in complexity, from data-constrained statistical models to full Earth system models (ESMs). These models can be used to quantify both long-term changes in the carbon cycle as well as interannual contributions to the growth rate of atmospheric CO2. I will discuss how exploiting latitudinal variations in CO2, as well as variations at finer timescales, allows us to separate the influence of temperature and drought on land-atmosphere carbon exchange. This approach also facilitates separation of fire emissions, which are associated with climate but also reflect human land management. These results provide a method to use atmospheric CO2 observations as constraints on future carbon-climate feedbacks and offer insight into the predictivity of current ESMs, including the CMIP5 ensemble. These results also underscore the importance of careful integration of data to improve carbon cycle model performance.