Examining the role of environmental memory in the predictability of carbon and water fluxes across Australian ecosystems

Abstract

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 both the immediate and 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 (such as delays between rainfall and peak plant water content or between a precipitation deficit and down-regulation of productivity) have largely been overlooked in the development of terrestrial biosphere 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 12 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). By focussing our analysis on a single continent (and predominately on a single genus), we reduced the degrees of variation between each site, providing a novel chance to explore the unique characteristics that might drive the importance of memory. Model fit varied considerably across sites when modelling NEE, with R2 values of between 0.30 and 0.83. λE was considerably more predictable across sites, with R2 values ranging from 0.56 to 0.93. When considered at a continental scale, both fluxes were more predictable when memory effects (expressed as lagged climate predictors) were included in the model. These memory effects accounted for an average of 17 % of the NEE predictability and 15 % for λE. Consistent with prior studies, the importance of environmental memory in predicting fluxes increased as site water availability declined (ρ=-0.73, p<0.01 for NEE, ρ=-0.67, p<0.05 for λE). However, these relationships did not necessarily hold when sites were grouped by vegetation type. We also tested a model of k-means clustering plus regression 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.

Non‐Stationary Lags and Legacies in Ecosystem Flux Response to Antecedent Rainfall

“…By contrast, an evergreen needleleaf forest in Canada, CA‐Qfo, had no significant difference between the instantaneous and historical models for NEP ( r 2 relative improvement of 0%, NME relative improvement of 4%). These improvements fall within the ranges reported in previous analysis (Cranko Page et al., 2022; Y. Liu et al., 2019).…”

“…Our results also show that lags and legacies have different impacts depending on the flux being modeled. Modeled NEP fluxes generally benefitted more from the introduction of antecedent climate than LE fluxes, consistent with a prior study (Cranko Page et al., 2022). This apparent discrepancy between the role of memory in the modeling of fluxes is due in part to the better performance of the instantaneous models for LE (more predictable) resulting in a lower potential benefit of memory effects.…”

“…Previous work has suggested that memory effects become more influential in increasingly arid ecosystems (Cranko Page et al., 2022; X. Liu et al., 2018. Y. Liu et al., 2019; D. Wu et al., 2015).…”

“…The clustering was performed with 729 clusters for four reasons (cf. Cranko Page et al (2022), which used between two and eight clusters). First, a greater number of clusters improves the model performance and, due to the regression expansion mechanism described above, did not risk overfitting.…”

“…However, legacy effects seen in tree rings are not necessarily present in other aspects of ecosystem functioning, such as fluxes or leaf area (Kannenberg et al, 2019(Kannenberg et al, , 2020(Kannenberg et al, , 2022 and can be hard to directly connect to underlying processes represented in models. Some studies have instead focused on systematic lags that do not vary through time, using methods such as Stochastic Antecedent Modeling (Cranko Page et al, 2022;Guo et al, 2020;Ogle et al, 2015;Ryan et al, 2015Ryan et al, , 2017 and lagged correlations (T. Zhang et al, 2015). These studies help our understanding of ecosystem functioning and are valuable in developing better LSMs (Keenan et al, 2012).…”