MITgcm model developed for state and parameter estimation in a pan-Arctic ocean and sea ice model using MITgcm (c63m)

Parameters in sea ice-ocean coupled models greatly affect the simulated ocean and sea ice evolution, and are normally tunned to bring the model state close to the observations. Using an adjoint method, spatiotemporally varying parameters of Arctic sea ice-ocean coupled model are optimized simultaneously with the initial condition and the atmospheric forcing by assimilating satellite and in-situ observations. The assimilation results show that the joint state and parameter estimation (SPE) substantially improves the sea ice concentration simulation. Particularly in October when the ocean surface starts to refreeze, SPE reduces the lead closing parameter Ho, which determines the minimum ice thickness formed in the open water, to increase the lateral sea ice growth and facilitate the seasonal rapid sea ice recovery in the Pacific sector. Comparisons with sea ice thickness observations from the moored upward looking sonars and Ice Mass Balance buoys demonstrate that the inclusion of model parameters in the optimization also leads to better sea ice thickness estimation. Overall, the adjoint-based SPE scheme has the potential to better reproduce the Arctic ocean and sea ice state and will be applied to reproduce a new Arctic sea ice-ocean reanalysis.海冰-海洋耦合模式中的参数对模拟得到的海洋与海冰演变过程影响显著，通常需对参数进行率定，以使模式状态贴近观测结果。本研究采用伴随方法（adjoint method），通过同化卫星与原位观测资料，同时对北极海冰-海洋耦合模式的时空变化参数、初始场以及大气强迫场开展协同优化。同化结果表明，联合状态与参数估计（joint state and parameter estimation, SPE）显著提升了海冰密集度的模拟效果。尤其在海洋表层开始重新冻结的10月，该方案将控制开阔水域形成最小海冰厚度的冰间水道闭合参数（lead closing parameter）Ho的取值降低，以此促进海冰侧向生长，助力太平洋扇区实现季节性快速海冰恢复。通过与锚定式向上声呐及冰质量平衡浮标（Ice Mass Balance buoys）获取的海冰厚度观测数据对比可知，在优化过程中纳入模式参数，同样改善了海冰厚度的模拟精度。总体而言，基于伴随方法的SPE方案能够更好地再现北极海洋与海冰状态，未来将应用于构建一套全新的北极海冰-海洋再分析数据集。