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.

Climate change is increasing the frequency and intensity of some meteorological extremes, with implications for the terrestrial carbon and water cycles (

&lt;p&gt;The vegetation&amp;#8217;s response to climate change is a major source of uncertainty in terrestrial biosphere model (TBM) projections. Constraining carbon cycle feedbacks to climate change requires improving our understanding of both the direct plant physiological responses to global change, as well as the role of legacy effects (e.g. reductions in plant growth, damage to the plant&amp;#8217;s hydraulic transport system), that drive multi-timescale feedbacks. In particular, the role of these legacy effects - both the timescale and strength of the memory effect - have been largely overlooked in the development of model hypotheses. This is despite the knowledge that plant responses to climatic drivers occur across multiple time scales (seconds to decades), with the impact of climate extremes (e.g. drought) resonating for many years. Using data from 13 eddy covariance sites, covering two rainfall gradients in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales of influence of antecedent drivers on fluxes of net carbon exchange and evapotranspiration. Using our data assimilation approach we were able to partition the influence of ecological memory into both biological and environmental components. Overall, we found that the importance of ecological memory to antecedent conditions increased as water availability declines. Our results therefore underline the importance of capturing legacy effects in TBMs used to project responses in water limited ecosystems.&lt;/p&gt;