Narapusetty Balachandrudu
1Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, VA USA.
2Innovim/Climate Prediction Center/NCEP/NWS, College Park, MD USA.
Theor Appl Climatol. 2018;133(1):113-121. doi: 10.1007/s00704-017-2169-7. Epub 2017 Jun 6.
The sensitivity of the sea-surface temperature (SST) prediction skill to the atmospheric internal variability (weather noise) in the North Pacific (20-60N;120E-80W) on decadal timescales is examined using state-of-the-art Climate Forecasting System model version 2 (CFS) and a variation of CFS in an Interactive Ensemble approach (CFSIE), wherein six copies of atmospheric components with different perturbed initial states of CFS are coupled with the same ocean model by exchanging heat, momentum and fresh water fluxes dynamically at the air-sea interface throughout the model integrations. The CFSIE experiments are designed to reduce weather noise and using a few ten-year long forecasts this study shows that reduction in weather noise leads to lower SST forecast skill. To understand the pathways that cause the reduced SST prediction skill, two twenty-year long forecasts produced with CFS and CFSIE for 1980-2000 are analyzed for the ocean subsurface characteristics that influence SST due to the reduction in weather noise in the North Pacific. The heat budget analysis in the oceanic mixed layer across the North Pacific reveals that weather noise significantly impacts the heat transport in the oceanic mixed layer. In the CFSIE forecasts, the reduced weather noise leads to increased variations in heat content due to shallower mixed layer, diminished heat storage and enhanced horizontal heat advection. The enhancement of the heat advection spans from the active Kuroshio regions of the east coast of Japan to the west coast of continental United States and significantly diffuses the basin-wide SST anomaly (SSTA) contrasts and leads to reduction in the SST prediction skill in decadal forecasts.
利用最先进的气候预测系统模型版本2（CFS）以及交互式集合方法（CFSIE）中的CFS变体，研究了北太平洋（北纬20 - 60度；东经120度 - 西经80度）年代际时间尺度上海面温度（SST）预测技能对大气内部变率（天气噪声）的敏感性。在CFSIE中，具有不同CFS初始扰动状态的六个大气分量副本通过在整个模型积分过程中在海气界面动态交换热量、动量和淡水通量，与同一个海洋模型耦合。CFSIE实验旨在减少天气噪声，本研究通过几个十年期的长期预测表明，天气噪声的减少会导致SST预测技能降低。为了理解导致SST预测技能降低的途径，分析了用CFS和CFSIE对1980 - 2000年进行的两个二十年期长期预测，以研究北太平洋天气噪声减少导致的影响SST的海洋次表层特征。对整个北太平洋海洋混合层的热量收支分析表明，天气噪声对海洋混合层中的热量输送有显著影响。在CFSIE预测中，天气噪声的减少由于混合层变浅、热量储存减少和水平热量平流增强，导致热量含量变化增加。热量平流的增强从日本东海岸的活跃黑潮区域延伸到美国大陆西海岸，显著扩散了全盆范围的海温异常（SSTA）差异，导致年代际预测中SST预测技能降低。
