Assessing the stability and uncertainty of predicted vegetation growth under climatic variability: northern mixed grass prairie

Abstract

Ecosystem models which include both variability of driving variables as an input, and uncertainty and/or stability in their predictions are rare, especially outside of forest and cropland applications. Our objective is to investigate the stability of productivity levels and temporal patterns in a northern mixed grass prairie site using scenarios of varying levels of climate variability. Predictions of annual net primary productivity (NPP) are compared under a variety of global change and management scenarios. Specifically, we investigate the relative responses of C 3 and C 4 vegetation functional groups as a diagnostic of changes in resource availability. Scenarios of gradual temperature increase over 200 years demonstrate that warming will have different effects depending partially on the seasonal timing of that warming, but mostly on the concurrent changes in moisture availability. We propose that stability of vegetation communities may be more important than simply predicting levels of productivity for answering many questions related to the impacts of global change. This is demonstrated using frequencies of consecutive years with low productivity. Moderate increase in precipitation variability without increases to average rainfall can increase productivity and apparently increase stability. Further increase in precipitation variability decreases stability. The uncertainty in NPP predictions can be quantified by repeated simulations using stochastic variations in driving climate variables. Uncertainty in NPP predictions is found to be at the order of 20 g/m 2/year, or about 25% of long-term averages. This lets us qualify our conclusions and shows that further research can reduce this uncertainty by better predictions of moisture availability, which can be obtained using finer spatial and temporal resolution representations.