Scholarlink

<p>This study investigates the mechanism by which high-frequency atmospheric noise affects the predictability of El Niño-Southern Oscillation (ENSO). Based on the community climate system model version 4 (CCSM4), two sets of comparative experiments were conducted: control (CTRL) and interactive ensemble (IE) simulations with reduced atmospheric noise. The analysis combining the linear inverse model (LIM) and the recharge oscillator model (ROM) shows that the IE method significantly improves the predictability of ENSO by effectively suppressing high-frequency atmospheric noise. Specifically, the LIM correlation coefficient of IE data is significantly improved compared to CTRL data within a 12-month forecast time frame. Mechanistic analysis revealed that under the IE mode, the system exhibits stronger thermocline feedback, with both the regulatory effect of thermocline depth anomalies on sea surface temperature (SST) and their response to SST significantly enhanced. This indicates that the long-term stability signals represented by subsurface heat content are more easily extracted under reduced noise conditions, thereby providing additional predictive information for ENSO forecasting.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Impact of high-frequency atmospheric noise on ENSO predictability

  • 期刊论文

  • 发表日期：

    2026年7月6日

查看全文
  • Guangyu Chi,
  • Ang Li,
  • Yishuai Jin,
  • Xiaopei Lin

摘要

This study investigates the mechanism by which high-frequency atmospheric noise affects the predictability of El Niño-Southern Oscillation (ENSO). Based on the community climate system model version 4 (CCSM4), two sets of comparative experiments were conducted: control (CTRL) and interactive ensemble (IE) simulations with reduced atmospheric noise. The analysis combining the linear inverse model (LIM) and the recharge oscillator model (ROM) shows that the IE method significantly improves the predictability of ENSO by effectively suppressing high-frequency atmospheric noise. Specifically, the LIM correlation coefficient of IE data is significantly improved compared to CTRL data within a 12-month forecast time frame. Mechanistic analysis revealed that under the IE mode, the system exhibits stronger thermocline feedback, with both the regulatory effect of thermocline depth anomalies on sea surface temperature (SST) and their response to SST significantly enhanced. This indicates that the long-term stability signals represented by subsurface heat content are more easily extracted under reduced noise conditions, thereby providing additional predictive information for ENSO forecasting.