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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2019-212
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2019-212
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 14 Jun 2019

Research article | 14 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

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

Jeroen Claessen1, Annalisa Molini2, Brecht Martens1, Matteo Detto3, Matthias Demuzere1,4, and Diego Miralles1 Jeroen Claessen et al.
  • 1Laboratory of Hydrology and Water Management, Department of Environment, Ghent University, Ghent, Belgium
  • 2Masdar Institute, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
  • 3Department of Ecology and Evolutionary Biology, Princeton University, Princeton, USA
  • 4Department of Geography, Ruhr-University Bochum, Bochum, Germany

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.

Jeroen Claessen et al.
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Jeroen Claessen et al.
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Short summary
Climate model performance in representing bi-directional interactions between climate and vegetation is benchmarked. A causal inference method is used to disentangle these interactions at multiple timescales. Overall results show that climate models satisfactory reproduce local climate controls on vegetation and vegetation feedbacks, although some common model deficiencies are flagged out. Our findings can help improve the representation of biosphere–atmosphere coupling in climate models.
Climate model performance in representing bi-directional interactions between climate and...
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