用4.5亿参数模型精准模拟大气变率与气候响应
ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses
- 自回归架构+守恒约束，实现多年稳定模拟
- 复现80年大气变化，包括飓风和厄尔尼诺响应
- 适合气候建模、机器学习与气象交叉研究者
现有机器学习天气模型无法评估其对海温、温室气体等外部条件变化的响应。本文提出ACE2（Ai2气候模拟器2版），一个4.5亿参数的自回归机器学习模型，以6小时时间分辨率、1°水平分辨率和8层垂直结构运行，精确守恒全球干空气质量与水汽。模型可稳定推进任意步数，每日仿真约1500年。它能生成热带气旋、梅云-朱利安振荡及平流层突然变暖等涌现现象，并准确再现过去80年大气对厄尔尼诺的响应及全球气温趋势。然而，其对海温与二氧化碳单独变化的敏感性尚不完全真实。
原文摘要 · Abstract (English)
Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse gases. Here we present ACE2 (Ai2 Climate Emulator version 2) and its application to reproducing atmospheric variability over the past 80 years on timescales from days to decades. ACE2 is a 450M-parameter autoregressive machine learning emulator, operating with 6-hour temporal resolution, 1° horizontal resolution and eight vertical layers. It exactly conserves global dry air mass and moisture and can be stepped forward stably for arbitrarily many steps with a throughput of about 1500 simulated years per wall clock day. ACE2 generates emergent phenomena such as tropical cyclones, the Madden Julian Oscillation, and sudden stratospheric warmings. Furthermore, it accurately reproduces the atmospheric response to El Niño variability and global trends of temperature over the past 80 years. However, its sensitivities to separately changing sea surface temperature and carbon dioxide are not entirely realistic.
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