Seasonal forecast daily and subdaily data on single levels

This entry covers single-level data and soil-level data at the original time resolution (once a day, or once every 6 hours, depending on the variable).
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 a 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 data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (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.

本条目涵盖了单层数据和土壤层数据，以原始的时间分辨率进行记录（每日一次或每六小时一次，具体取决于变量）。季节性预测提供了对地球系统在数周或数月时间尺度上变化的长期展望，这种展望源于系统某些缓慢变化的组成部分的可预测变化。例如，海洋温度通常以周或月的时间尺度缓慢变化；由于海洋对上层大气的影响，其属性（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’大气条件的改变构成了所有长期（如季节性）预测的核心。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。然而，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。这正是长期预测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，长期预测使用集合，而有意义的预测产品反映了结果分布。鉴于地球系统各个组成部分之间复杂且非线性的相互作用，最适合进行长期预测的工具是气候模型，这些模型包含尽可能多的系统关键组成部分；通常，此类模型包括大气、海洋和地表的表示。这些模型以描述系统在预测起始点状态的初始数据为基础，用于预测该状态随时间的变化。虽然可以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善的误差，但模型中近似造成的误差很大程度上取决于模型的选择。一种方便量化这些近似效应的方法是结合几个独立开发、初始化和运行的模型输出。为此，C3S提供了多系统季节性预测服务，该服务收集、处理和组合了在欧洲几个国家的预报中心开发的、实施和运行的尖端季节性预测系统产生的数据，以实现用户相关的应用。C3S季节性多系统组成和支撑服务的数据库全部内容在文档中进行了描述。数据被分为几个目录条目（CDS数据集），目前按变量的类型（单层或多层，在压力表面上）以及应用的后期处理水平（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）进行分组。数据包括从本条目发布开始每月实时创建的预测以及针对每个来源和系统的过去指定时间段内初始化的回顾性预测（回溯预测）。