Summary
This continental-scale study quantified the role of environmental memory—lagged effects of antecedent climate conditions including extreme events and structural delays—in predicting carbon and water fluxes across Australian ecosystems. Using data from 12 sites spanning two rainfall gradients and hierarchical Bayesian modelling, the authors demonstrated that memory effects substantially improve flux predictability, with latent heat flux (R² 0.56–0.93) considerably more predictable than net ecosystem exchange (R² 0.30–0.83). The findings underscore that ignoring multi-timescale plant physiological responses and climate extremes' lingering effects introduces significant uncertainty into terrestrial biosphere model projections.
UK applicability
The study's focus on semi-arid to temperate Australian ecosystems with relatively low to moderate rainfall may have limited direct applicability to UK temperate grasslands and arable systems, which typically experience higher and more reliable precipitation. However, the methodological framework for quantifying environmental memory in ecosystem flux models could inform UK climate-carbon cycle projections and improve parameterisation of UK terrestrial biosphere models, particularly for understanding responses to increasing drought risk.
Key measures
Net ecosystem exchange (NEE), latent heat flux (λE), lagged climate predictors, model fit (R² values), rainfall gradients (256–1491 mm yr⁻¹)
Outcomes reported
The study characterised the timescales and magnitude of antecedent climate drivers (memory effects) influencing daily net ecosystem exchange (NEE) and latent heat flux (λE) across 12 eddy covariance sites. Memory effects accounted for an average of 17% of NEE predictability and 15% of λE predictability when included in hierarchical Bayesian models.
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