Temporal Memory Mechanisms and Biome‐Specific Drivers of Ecosystem Carbon Flux: Insights From Explainable Deep Learning Modeling

Abstract

Ecosystem carbon dynamics are governed by complex temporal dependencies and environmental interactions, yet these processes remain poorly understood and quantified across diverse biomes. Here, we developed an explainable LSTM‐Attention framework that integrates LSTM networks, attention mechanisms, and gradient‐based attribution methods to reveal temporal dependencies in ecosystem carbon flux responses by analyzing eddy covariance flux measurements from 71 sites spanning eight North American biomes. Using this approach, we identified three distinct temporal memory patterns governing carbon flux responses. Grasslands exhibit short‐term memory dominance with exponentially increasing temporal contributions from distant to recent past. Deciduous broadleaf forests and wetlands show long‐term memory dominance, with deciduous broadleaf forests displaying the strongest historical dependence (attribution values declining from 0.30 at 6 months to 0.045 at 1 month). Croplands and evergreen needleleaf forests demonstrate U‐shaped dual memory patterns. Building on these temporal patterns, we identified biome‐specific environmental drivers operating within each memory framework: wetlands primarily controlled by soil moisture, evergreen needleleaf forests by radiation, and closed shrublands by vapor pressure deficit. Beyond individual drivers, we uncovered critical nonlinear interactions that diverged from linear correlations at most sites (55 of 71 with ρ

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