Seasonal forecast monthly averages of ocean variables

This entry covers global ocean data aggregated to a monthly time resolution. The catalogue entry includes temperature and salinity characteristics of the upper oceans and complements the other seasonal forecast catalogue entries for the land and atmospheric variables.
Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect distributions of outcomes.
Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.
To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The seasonal forecast data is grouped in several catalogue entries (CDS datasets), currently defined by the model component and type of variable: outputs from the ocean component or the atmospheric one (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).
The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.

本条目涵盖全球海洋数据，按月时间分辨率进行汇总。目录条目包括上层海洋的温度和盐度特性，并补充了其他季节性预报目录条目中的陆地和大气变量。季节性预报提供了对地球系统在几周或几个月时间跨度内变化的长期展望，这是由于系统某些缓慢变化的组成部分的可预测变化所致。例如，海洋温度通常在周或月的时间尺度上缓慢变化；由于海洋对上层大气的影响，其属性（例如温度）的变化可以改变局部和远程的大气条件。这种对‘常规’大气条件的修改是所有长期（例如季节性）预报的核心。这与天气预报不同，天气预报提供了对未来几天大气状态演变的时间空间上的更多精确细节。超过几天，大气的混沌性质限制了在局部尺度上预测精确变化的可能性。这是长期预报大气条件具有较大不确定性的原因之一。为了量化这种不确定性，长期预报使用集合，有意义的预报产品反映了结果的分布。鉴于地球系统各个组成部分之间复杂的非线性相互作用，最适合长期预报的工具是包含系统尽可能多的关键组成部分的气候模型；通常，此类模型包括大气、海洋和地表的表示。这些模型使用描述系统起始点状态的初始数据初始化，并用于预测该状态随时间的变化。虽然可以通过使用集合来描述来自地球系统组成部分初始条件知识不完美的不确定性，但模型中近似引起的不确定性在很大程度上取决于模型的选择。一种方便量化这些近似影响的方法是结合来自几个独立开发、初始化和运行的模型的输出。为此，C3S提供了一种多系统季节性预报服务，其中收集、处理和组合了在几个欧洲国家的预报中心开发的、实施和运行的最新季节性预报系统的数据，以使用户相关应用成为可能。C3S季节性多系统的组成以及支撑服务的数据库的全部内容在文档中进行了描述。季节性预报数据按几个目录条目（CDS数据集）分组，目前由模型组件和变量的类型定义：来自海洋组件或大气组件的输出（单层或多层，在压力面上）以及应用的后期处理级别（原始时间分辨率的数据，时间聚合处理以及与偏差调整相关的后期处理）。数据包括从本条目发布开始每月实时创建的预报以及初始化于过去在各个来源和系统文档中指定的时期的回顾性预报（hindcasts）。