Global biosphere–climate interaction: a multi-scale appraisal of observations and models

Abstract

Abstract. Improving the skill of Earth System Models (ESMs) in representing climate–vegetation interactions is crucial to enhance our predictions of future climate and ecosystem functioning. Therefore, ESMs need to correctly simulate the impact of climate on vegetation, but likewise, feedbacks of vegetation on climate must be adequately represented. However, model predictions at large spatial scales remain subjected to large uncertainties, mostly due to the lack of observational patterns to benchmark them. Here, the bi-directional nature of climate–vegetation interactions is explored across multiple temporal scales by adopting a spectral Granger causality framework that allows identifying potentially co-dependent variables. Results based on global and multi-decadal records of remotely-sensed leaf area index (LAI) and observed atmospheric data show that the climate control on vegetation variability increases with longer temporal scales, being higher at inter-annual than multi-month scales. The phenological cycle in energy-driven latitudes is mainly controlled by radiation, while in (semi-)arid regimes precipitation variability dominates at all temporal scales. However, at inter-annual scales, the control of water availability gradually becomes more wide-spread than that of energy constraints. The observational results are used as a benchmark to evaluate ESM simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Findings indicate a tendency of ESMs to over-represent the climate control on LAI dynamics, and a particular overestimation of the dominance of precipitation in arid and semi-arid regions. Analogously, CMIP5 models overestimate the control of air temperature on forest seasonal phenology. Overall, climate impacts on LAI are found to be stronger than the feedbacks of LAI on climate in both observations and models, arguably due to the local character of the analysis that does not allow for the identification of downwind or remote vegetation feedbacks. Nonetheless, wide-spread effects of LAI variability on radiation are observed over the northern latitudes, presumably related to albedo changes, which are well-captured by the CMIP5 models. Overall, our experiments emphasise the potential of benchmarking the representation of particular interactions in online ESMs using causal statistics in combination with observational data, as opposed to the more conventional evaluation of the magnitude and dynamics of individual variables.