用变分自编码器检测气候模型中植物生产力极端事件，效果优于传统方法。
Variational Autoencoders-based Detection of Extremes in Plant Productivity in an Earth System Model
- 用深度学习的变分自编码器自动捕捉碳循环非线性变化模式。
- 2050-2080年间西美和中美地区极端负向碳通量事件频率与强度显著上升。
- 无需预设周期，可从数据中自主发现信号特征，适合复杂气候模拟分析。
气候异常显著影响陆地碳循环动态，亟需可靠方法识别与分析植物生产力中的异常行为。本研究将变分自编码器（VAE）首次应用于美国大陆四个AR6区域的社区地球系统模型2版模拟数据，检测总初级生产力（GPP）极端事件。对比了基于VAE与传统奇异谱分析（SSA）在1850–1880、1950–1880及2050–1880（SSP585情景）三个时间段的表现。VAE采用三层全连接网络与12个月输入序列的潜在空间，对归一化GPP时间序列进行重建，并依据重构误差识别异常。极端事件定义为低于5%分位数的异常值。结果表明，尽管VAE阈值较高（179–756 GgC vs. SSA的100–784 GgC），但两者在极端事件空间分布上高度一致。二者均显示至2050–1880，负向碳循环极端事件在强度和频率上显著增加，尤以西美与中美地区为甚。VAE表现与传统方法相当，同时具备计算优势与捕捉非线性时序依赖的能力，且无需预先设定信号周期，由数据自动发现。
原文摘要 · Abstract (English)
Climate anomalies significantly impact terrestrial carbon cycle dynamics, necessitating robust methods for detecting and analyzing anomalous behavior in plant productivity. This study presents a novel application of variational autoencoders (VAE) for identifying extreme events in gross primary productivity (GPP) from Community Earth System Model version 2 simulations across four AR6 regions in the Continental United States. We compare VAE-based anomaly detection with traditional singular spectral analysis (SSA) methods across three time periods: 1850-80, 1950-80, and 2050-80 under the SSP585 scenario. The VAE architecture employs three dense layers and a latent space with an input sequence length of 12 months, trained on a normalized GPP time series to reconstruct the GPP and identifying anomalies based on reconstruction errors. Extreme events are defined using 5th percentile thresholds applied to both VAE and SSA anomalies. Results demonstrate strong regional agreement between VAE and SSA methods in spatial patterns of extreme event frequencies, despite VAE producing higher threshold values (179-756 GgC for VAE vs. 100-784 GgC for SSA across regions and periods). Both methods reveal increasing magnitudes and frequencies of negative carbon cycle extremes toward 2050-80, particularly in Western and Central North America. The VAE approach shows comparable performance to established SSA techniques, while offering computational advantages and enhanced capability for capturing non-linear temporal dependencies in carbon cycle variability. Unlike SSA, the VAE method does not require one to define the periodicity of the signals in the data; it discovers them from the data.
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