Seasonal forecast monthly statistics on single levels

This entry covers single-level data aggregated on a monthly time resolution.
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.

本条目涵盖按月时间分辨率汇总的单层数据。季节性预报针对地球系统在数周或数月时间尺度上的变化趋势，提供长期预测视角，这些变化趋势源于系统内某些缓慢变化组成部分的可预测变化。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直接影响，其属性（如温度）的变化可以改变局部和远程的大气条件。此类对‘常规’大气条件的修改构成了所有长期（如季节性）预报的精髓。这与对未来几天大气状态演变的预测相比，后者在时间和空间上提供了更为精确的细节。数天之后，大气的混沌特性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。为了量化此类不确定性，长期预报采用集合预报方法，而具有实际意义的预报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、非线性的相互作用，用于长期预报的最佳工具是气候模型，这些模型尽可能包含系统的关键组成部分；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。尽管可以使用集合预报来描述来自对地球系统组成部分初始条件知识不完善的不确定性，但模型中近似产生的误差很大程度上取决于模型的选择。一种方便的方法是将多个独立开发、初始化和运行的模型输出结合在一起，以量化这些近似的影响。为此，C3S提供了一种多系统季节性预报服务，其中收集、处理和组合了在欧洲多个国家的预报中心开发、实施和运营的最先进的季节性预报系统产生的数据，以实现用户相关的应用。C3S季节性多系统组成以及支撑该服务的数据库的全部内容均在文档中描述。数据被分为几个目录条目（CDS数据集），目前由变量的类型（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）定义。数据包括从本条目发布开始每月实时创建的预报，以及根据每个起源和系统的文档中指定的过去时间段初始化的回顾性预报（回溯预报）。