The next‐generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. The ensemble‐based ocean data assimilation (ODA) system is updated for Modular Ocean Model Version 6 (MOM6), the ocean component of SPEAR. Ocean initial conditions for seasonal predictions, as well as an ocean state estimation, are produced by the MOM6 ODA system in coupled SPEAR models. Initial conditions of the atmosphere, land, and sea ice components for seasonal predictions are constructed through additional nudging experiments in the same coupled SPEAR models. A bias correction scheme called ocean tendency adjustment (OTA) is applied to coupled model seasonal predictions to reduce model drift. OTA applies the climatological temperature and salinity increments obtained from ODA as three‐dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on preliminary retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially sea surface temperature (SST) forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño–Southern Oscillation (ENSO).

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GFDL的SPEAR季节预测系统：用于耦合模型预测的初始化和海洋趋势调整（OTA）

下一代季节预报系统是国家海洋与大气管理局（NOAA）的地球物理流体动力学实验室（GFDL）的无缝预测和EArth系统研究（SPEAR）系统的一部分。SPEAR致力于开发一个无缝系统，用于跨时标的预测和研究。基于集合的海洋数据同化（ODA）系统已针对SPEAR的海洋组件模块化海洋模型版本6（MOM6）更新。MOM6 ODA系统在耦合的SPEAR模型中产生了用于季节预测以及海洋状态估计的海洋初始条件。在相同的耦合SPEAR模型中，通过额外的微调实验，构造了用于季节性预测的大气，陆地和海冰成分的初始条件。将一种称为海洋趋势调整（OTA）的偏差校正方案应用于耦合的模型季节预测，以减少模型漂移。OTA将从ODA获得的气候温度和盐度增量作为三维趋势项，应用于耦合后的SPEAR模型的MOM6海洋分量。根据初步的回顾性季节性预测，我们证明OTA可以减少耦合模型预测中的模型漂移（尤其是海表温度（SST）预测漂移），并提高诸如厄尔尼诺-南方涛动（ENSO）等应用的季节性预测能力。OTA将从ODA获得的气候温度和盐度增量作为三维趋势项，应用于耦合后的SPEAR模型的MOM6海洋分量。根据初步的回顾性季节性预测，我们证明OTA可以减少耦合模型预测中的模型漂移（尤其是海表温度（SST）预测漂移），并提高诸如厄尔尼诺-南方涛动（ENSO）等应用的季节性预测能力。OTA将从ODA获得的气候温度和盐度增量作为三维趋势项，应用于耦合后的SPEAR模型的MOM6海洋分量。根据初步的回顾性季节性预测，我们证明OTA可以减少耦合模型预测中的模型漂移（尤其是海表温度（SST）预测漂移），并提高诸如厄尔尼诺-南方涛动（ENSO）等应用的季节性预测能力。