大西洋经向翻转环流变率中的持久记忆与尾部风险放大：基于CMIP6校准的Volterra积分框架
Persistent memory and tail-risk amplification in Atlantic Meridional Overturning Circulation variability: a Volterra integral framework calibrated with CMIP6
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中文总结 AI 辅助
该研究用Volterra积分框架量化AMOC的持久记忆，发现其显著放大尾部风险，并支持记忆感知的AMOC风险评估与早期预警。
中文摘要 AI 辅助
大西洋经向翻转环流（AMOC）携带多年代际记忆，而集合平均的风险预测未能捕捉这一记忆。我们通过一个Volterra积分框架量化了这一记忆及其对尾部风险的影响，该框架应用于14个CMIP6模型（1850-2100年，三种SSP情景）中减去集合平均后的变率，从而使结果反映内在的热盐记忆而非共同的人为强迫趋势。通过留一法交叉验证和退火-淬火尾部分解，我们得到四个结果。第一，集合平均的DFA1 Hurst指数为H = 0.781 ± 0.219，14个模型中有12个显示出长程依赖性（H > 0.5），与约33年的热盐调整时间尺度一致。第二，一阶Volterra模型在样本外均方根误差（RMSE）上比最佳自回归基线降低了16.8%，比无约束的20阶滞后分布基线降低了12.3%，两者均对200次循环移位安慰剂检验稳健（p < 0.05）：物理驱动的核形状具有真正的预测优势。第三，滚动30年下尾频率根据情景不同升至历史基线的1.9-3.1倍，且在SSP5-8.5情景下，14个模型中有8个的记忆放大指数超过1（中位数为1.15）：弱AMOC状态的持续性驱动的聚集将尾部频率放大至超出仅强迫预测的水平。第四，模型特定的最优记忆视界（15-52年）与热盐状态相关（r = 0.74，p < 0.01），且集合平均Hurst指数在14个模型中的9个中已超过检测阈值H* = 0.70，根据情景不同，达到接近临界条件的理论提前时间为10-35年。这些结果支持在中高强迫下进行轨迹特定、记忆感知的AMOC风险评估，并支持一个物理基础扎实、情景条件化的早期预警框架。
英文摘要
The Atlantic Meridional Overturning Circulation (AMOC) carries multi-decadal memory that ensemble-mean risk projections do not capture. We quantify this memory and its consequences for tail risk with a Volterra integral framework applied to ensemble-mean-subtracted variability in 14 CMIP6 models (1850-2100, three SSP scenarios), so that results reflect intrinsic thermohaline memory rather than the common anthropogenic forcing trend. Four results follow from leave-one-out cross-validation and an annealed-quenched tail decomposition. First, the ensemble-mean DFA1 Hurst exponent is H = 0.781 +/- 0.219, with 12 of 14 models showing long-range dependence (H > 0.5), consistent with thermohaline adjustment timescales near 33 yr. Second, a first-order Volterra model reduces out-of-sample RMSE by 16.8% over the best autoregressive baseline and by 12.3% over an unconstrained 20-lag distributed baseline, both robust to 200 circular-shift placebos (p < 0.05): the physically motivated kernel shape carries genuine predictive advantage. Third, the rolling 30-year lower-tail frequency rises 1.9-3.1x above the historical baseline depending on scenario, and the memory amplification index exceeds 1 in 8 of 14 models under SSP5-8.5 (median 1.15): persistence-driven clustering of weak-AMOC states amplifies tail frequency beyond forcing-only projections. Fourth, model-specific optimal memory horizons (15-52 yr) correlate with thermohaline regime (r = 0.74, p < 0.01), and the ensemble-mean Hurst exponent already exceeds a detection threshold H* = 0.70 in 9 of 14 models, with theoretical lead times to near-tipping conditions of 10-35 yr depending on scenario. These results support trajectory-specific, memory-aware AMOC risk assessment under moderate-to-high forcing and a physically grounded, scenario-conditional early-warning framework.
