Preprints
https://doi.org/10.5194/bg-2021-254
https://doi.org/10.5194/bg-2021-254

  01 Oct 2021

01 Oct 2021

Review status: this preprint is currently under review for the journal BG.

Examining the Role of Environmental Memory in the Predictability of Carbon and Water Fluxes Across Australian Ecosystems

Jon Cranko Page1,2, Martin G. De Kauwe3,1,2, Gab Abramowitz1,2, Jamie Cleverly4, Nina Hinko-Najera5, Mark J. Hovenden6, Yao Liu7, Andy J. Pitman1,2, and Kiona Ogle8 Jon Cranko Page et al.
  • 1ARC Centre of Excellence for Climate Extremes, Sydney, NSW 2052, Australia
  • 2Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
  • 3School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK
  • 4Terrestrial Ecosystem Research Network, College of Science and Engineering, James Cook University, Cairns, QLD 4870, Australia
  • 5School of Ecosystem and Forest Sciences, The University of Melbourne, 4 Water Street, Creswick, VIC 3363, Australia
  • 6Biological Sciences, School of Natural Sciences, University of Tasmania, Hobart, TAS 7005, Australia
  • 7Department of Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
  • 8School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, 86011, U.S.A.

Abstract. The vegetation’s response to climate change is a significant source of uncertainty in future terrestrial biosphere model projections. Constraining climate-carbon cycle feedbacks requires improving our understanding of direct, as well as long-term, plant physiological responses to climate. In particular, the timescales and strength of memory effects arising from both extreme events (i.e., droughts and heatwaves) and structural lags in the systems have largely been overlooked in the development of models. This is despite the knowledge that plant responses to climatic drivers occur across multiple timescales (seconds to decades), with the impact of climate extremes resonating for many years.

Using data from 13 eddy covariance sites, covering two rainfall gradients (256 to 1491 mm yr−1) in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales and magnitude of influence of antecedent drivers on daily net ecosystem exchange (NEE) and latent heat flux (λE). Model fit varied considerably across sites when modelling NEE, with R2 values of between 0.30 and 0.83. Latent heat was considerably more predictable across sites, with R2 values ranging from 0.56 to 0.95. When considered at a continental scale, both fluxes were more predictable when memory effects were included in the model. These memory effects accounted for an average of 17 % of the NEE predictability and 15 % for λE. The importance of environmental memory in predicting fluxes increased as site water availability declined (ρ = −0.72, p < 0.01 for NEE, ρ = −0.62, p < 0.05 for λE). However, these relationships did not necessarily hold when sites were grouped by vegetation type. We also tested a k-means clustering plus regression model to confirm the suitability of the Bayesian model for modelling these sites. The k-means approach performed similarly to the Bayesian model in terms of model fit, demonstrating the robustness of the Bayesian framework for exploring the role of environmental memory. Our results underline the importance of capturing memory effects in models used to project future responses to climate change, especially in water-limited ecosystems. Finally, we demonstrate a considerable variation in individual site predictability, driven to a notable degree by environmental memory, and this should be considered when evaluating model performance across ecosystems.

Jon Cranko Page et al.

Status: open (until 12 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-254', Anonymous Referee #1, 04 Oct 2021 reply
    • AC1: 'Reply on RC1', Jon Cranko Page, 11 Oct 2021 reply

Jon Cranko Page et al.

Model code and software

Model and Analysis Code Jon Cranko Page https://github.com/JDCP93/OzFlux_SAM

Jon Cranko Page et al.

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Short summary
Although vegetation responds to climate at a wide range of timescales, models of the land carbon sink often ignore responses that don’t occur instantly. In this study, we explored the timescales at which Australian ecosystems respond to climate. We identified that carbon and water fluxes can be modelled more accurately if we include environmental drivers from up to a year in the past. The importance of antecedent conditions is related to ecosystem aridity but is also influenced by other factors.
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