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 bidirectional nature of climate–vegetation interactions is explored across multiple temporal scales by adopting a spectral Granger causality framework that allows identification of 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. Globally, precipitation is the most dominant driver of vegetation at monthly scales, particularly in (semi-)arid regions. The seasonal LAI variability in energy-driven latitudes is mainly controlled by radiation, while air temperature controls vegetation growth and decay in high northern latitudes at inter-annual scales. These observational results are used as a benchmark to evaluate four 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 at inter-annual scales. Analogously, CMIP5 models overestimate the control of air temperature on seasonal vegetation variability, especially in forested regions. Overall, climate impacts on LAI are found to be stronger than the feedbacks of LAI on climate in both observations and models; in other words, local climate variability leaves a larger imprint on temporal LAI dynamics than vice versa. Note however that while vegetation reacts directly to its local climate conditions, the spatially collocated character of the analysis does not allow for the identification of remote feedbacks, which might result in an underestimation of the biophysical effects of vegetation on climate. Nonetheless, the widespread effect of LAI variability on radiation, as observed over the northern latitudes due to albedo changes, is overestimated 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.
Highlights
The biosphere is a key factor in the global carbon and water cycles, mainly through its impact on the energy balance at the Earth’s surface and the chemistry of the atmosphere (McPherson, 2007; Pearson et al, 2013; Le Quéré et al, 2018)
Most of these efforts focus on the evaluation of the magnitude and short-term dynamics of individual variables, rather than on the inter-variable sensitivities, which would be more informative on whether the interplay between vegetation and climate is reliably represented in these models
Results are shown separately for monthly (Fig. 2a), seasonal (Fig. 2c), and inter-annual (Fig. 2e) timescales using a trivariate colour map according to the fraction explained by each climatic driver
Summary
The biosphere is a key factor in the global carbon and water cycles, mainly through its impact on the energy balance at the Earth’s surface and the chemistry of the atmosphere (McPherson, 2007; Pearson et al, 2013; Le Quéré et al, 2018). The biosphere provides a negative climate feedback by acting as a net carbon sink (Schimel et al, 2015) This strong regulating power of vegetation in the Earth system indicates the need to accurately incorporate biosphere–climate interactions in the models used to predict changes in terrestrial ecosystems and future climate (Piao et al, 2013; Pachauri et al, 2014; Le Quéré et al, 2018). Previous benchmark studies have typically focused on one specific timescale (typically annually or monthly), while the ecosystem response to (and feedback on) climate is expected to vary for different timescales; e.g. a model may accurately replicate the observed interplay between vegetation and climate at monthly scales but still fail to capture the sensitivities that become relevant at seasonal or inter-annual timescales
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