In the dynamic interplay between climate and ecosystems, the effects of present weather conditions on vegetation productivity have long been recognized. However, an emerging dimension that complexities our understanding is the influence of antecedent climate—the climatic conditions that preceded current observations—on ecosystem functions. This concept, often referred to as climate memory or memory effects, suggests that ecosystems do not merely respond instantaneously to environmental changes but carry legacies of previous climatic states that shape current productivity. Recent cutting-edge research spearheaded by Qiu, Zhang, Cai, and colleagues breaks new ground in uncovering the magnitude and mechanics behind these lagged ecological responses during extreme climate events.
The conventional approach to ecosystem productivity has predominantly focused on immediate weather variables such as temperature, precipitation, and vapor pressure deficit. While these are undeniably crucial, they capture only a snapshot of the complex processes influencing vegetation growth and carbon cycling. Memory effects imply a temporal dimension where past environmental conditions modulate plant physiological status, soil moisture reservoirs, nutrient availability, and microbial community dynamics over extended periods. This legacy can either buffer ecosystems against or amplify the impacts of climatic extremes, complicating predictions of ecosystem responses in a rapidly changing climate.
To untangle these complex relationships, the research team implemented a sophisticated machine learning framework that leverages extensive datasets from eddy covariance towers, which provide high-frequency measurements of ecosystem gross primary productivity (GPP)—a key indicator of carbon uptake by plants. By analyzing data spanning a quarter-century from 1995 to 2020, the model could account for both immediate and antecedent climatic influences, offering unprecedented insights into the cascading and cumulative effects that shape ecosystem productivity during extreme climatic events.
One of the most groundbreaking findings of this study is the substantial role of antecedent climate, which accounts for approximately 38.2% of ecosystem productivity variability during extreme events. This is a compelling quantification underscoring that historical climatic conditions are nearly as influential as current weather in dictating vegetation productivity. The researchers delved deeper to parse out which climatic variables within this antecedent category wield the most pronounced effects. Among these, precipitation emerged as the dominant driver, responsible for 42.2% of the memory effects influencing productivity anomalies.
Such a prominent role of precipitation in antecedent conditions is scientifically intuitive and ecologically significant. Water availability over previous months can determine soil moisture reserves, deeply influencing plant water stress and growth capacity under current climatic extremes. Following precipitation, temperature and vapor pressure deficit (VPD)—a measure of atmospheric dryness—account for sizeable fractions of the memory effect at 22.1% and 20.8%, respectively. These findings intricately tie past heat exposure and moisture stress with resilience or vulnerability to present-day environmental pressures.
A nuanced aspect of this study is its dissection of the temporal scales over which memory effects operate. Extreme climatic events that are conditioned by long-term climatic variability tend to cause more severe productivity disruptions than short-lived extremes. This suggests that the temporal depth of antecedent climate exposure fundamentally alters how ecosystems endure or succumbing to stress. Importantly, semi-arid ecosystems—often characterized by sparse vegetation and limited water economics—exhibit the most pronounced productivity anomalies, accompanied by extended memory responses that prolong ecological recovery.
The implications of these results extend into global biogeochemical cycles and carbon budgets. Gross primary productivity directly determines terrestrial carbon sequestration potentials, influencing atmospheric CO2 concentrations and feedbacks into the climate system. By recognizing antecedent climate conditions as a pivotal regulator of GPP anomalies, this research provides a more integrated and predictive framework to anticipate ecosystem carbon fluxes under intensifying climate variability and extreme events.
Moreover, the methodological innovation embodied in this study—a transparent, interpretable machine learning approach—sets a new benchmark for studying complex, lagged climate-ecosystem interactions. Unlike black-box models, the interpretable nature of the model enables scientists to explicitly quantify the relative importance of various climatic factors over different time horizons, facilitating targeted investigations into mechanistic drivers behind observed patterns. This approach represents a critical advance in Earth system science, offering robust tools for improving ecosystem models within climate projections.
These advances come at a critical time as global ecosystems face escalating frequency and severity of heatwaves, droughts, and storms tied to anthropogenic climate change. Understanding how memory effects operate across biomes and climatic gradients will be crucial for predicting vegetation responses and managing ecosystem services that underpin human well-being. The disproportionate vulnerability of semi-arid regions revealed by this study also underscores the need for prioritized conservation and adaptation strategies in these fragile landscapes.
Looking ahead, the integration of longer time-series remote sensing data, experimental manipulations, and microbial ecology studies could further unravel the biological and physical pathways through which antecedent climate impresses upon ecosystems. The entanglement of soil hydrology, plant physiology, and microbial feedbacks in shaping memory effects remains a fertile ground for interdisciplinary research.
Furthermore, the study highlights that memory effects are not unidirectional or uniform; they can both exacerbate and mitigate the impacts of current extremes depending on prior conditions. For example, antecedent precipitation might precondition soils to retain moisture that buffers plants against a forthcoming drought, or conversely, prolonged drought memory could compound stress leading to more severe productivity losses. This duality emphasizes the complexity of natural systems and cautions against simplistic projections of ecosystem responses.
In sum, this landmark research enriches our conceptual and quantitative understanding of ecosystem dynamics in a changing climate by revealing the pivotal influence of antecedent climate on productivity anomalies during extreme events. The findings recalibrate how scientists, policymakers, and resource managers might incorporate historical climate legacies into carbon cycle assessments and ecosystem resilience planning.
By illuminating the hidden temporal layers of climate effects embedded within ecosystems, this study lays a foundation for more nuanced and effective approaches to safeguarding the planet’s biological productivity amid mounting environmental challenges. As extreme climatic episodes grow more frequent and intense globally, embracing the lessons from memory effects will be essential for anticipating and mitigating the vulnerabilities of Earth’s vegetation and the vital functions they sustain.
Subject of Research:
Influence of antecedent climate conditions on ecosystem productivity anomalies during extreme climatic events.
Article Title:
Large contribution of antecedent climate to ecosystem productivity anomalies during extreme events.
Article References:
Qiu, J., Zhang, Y., Cai, M. et al. Large contribution of antecedent climate to ecosystem productivity anomalies during extreme events. Nat. Geosci. (2025). https://doi.org/10.1038/s41561-025-01856-4
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