ERror Growth in Operational Data Initialised Coupled Systems (ERGODICS)

气候模式的耦合初始化是一个具有挑战性的新领域，需要应用复杂的数据同化方法，同时实际管理耦合系统的复杂相互作用物理和动力学，特别是在海气界面和边界层。该建议的重点是了解并减少在一定范围的前置时间内耦合的后置偏差误差。它将利用ECMWF和英国气象局已经执行或计划的后置集，对同化增量或偏差方案进行修改。在强迫模型尽可能接近观测值的地方，将使用全场后验。依赖于前置时间的气候漂移包含关于模式过程误差的潜在重要信息。本提案将开发和测试几种方法来分析和减少这种前置时间相关的漂移，包括将初始条件漂移与受扰物理漂移相匹配，将大气-海洋初始化增量与耦合模型协方差相匹配，修改协方差以关注更长的前置时间预测，以及修改操作系统中已经使用的偏差方案。这种侧重于减少耦合偏差的方法的多样性是该提案的一个优势，使有希望的结果得以更充分地发展。这种方法，团队的经验，以及对现有操作耦合系统的修改的关注，也将使该项目作为整个NGWCP计划的集成项目有效地运行，从而使其他资助项目的有希望的结果能够在操作模型环境中得到实现和测试。

Coupled initialisation of climate models is a challenging new area requiring the application of sophisticated data assimilation methods, while at the same time practically managing complex interacting physics and dynamics of the coupled system, particularly at the air-sea interface and boundary layers. This proposal focuses on understanding and then reducing coupled hindcast bias errors at a range of lead times. It will exploit hindcast sets already performed or planned at ECMWF and the Met Office, making modifications to the assimilation increments or bias schemes. Full-field hindcasts will be used where the model is forced as close to the observations as possible. The lead-time dependent climate drift contains potentially important information about the model process errors. This proposal will develop and test several methods to analyse and reduce this lead-time dependent drift, including matching initial condition drift to perturbed physics drifts, matching atmosphere-ocean initialization increments to coupled model covariances, modifying covariances to focus on longer lead time predictions, and modifying already used bias schemes within operational systems. This diversity of approaches, focused on reducing coupled bias, is a strength of the proposal allowing promising results to be developed more fully. This approach, the experience of the team, and the focus on modifications to already existing operational coupled systems will also allow this project to operate effectively as an integrating project for the whole NGWCP program, allowing promising results from other funded projects to be brought through and tested within operational model environments.