Seasonal forecast anomalies on single levels

This entry covers single-level data post-processed for bias adjustment 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 variables available in this data set are listed in the table below. The data includes forecasts created in real-time each month starting from the publication of this entry.

本条目涵盖了对单级数据进行后处理以调整偏差的月度时间分辨率数据。季节性预报提供了对地球系统在几周或几个月时间尺度上变化的长期展望，这是由于系统某些缓慢变化的组成部分的可预测变化所导致的。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’大气条件的修改正是所有长期（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在时间和空间上的更为精确的细节。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。这就是长期预报大气条件存在大量不确定性的原因之一。为了量化这种不确定性，长期预报使用集合体，而具有意义的预报产品反映了结果分布。地球系统各个组成部分之间复杂的非线性相互作用，使得气候模型成为长期预报的最佳工具，这些模型尽可能包含系统的所有关键组成部分；通常，这些模型包括大气、海洋和地表的表示。这些模型使用描述系统在预报起点状态的初始数据来初始化，并用于预测该状态随时间的变化。虽然可以通过集合体使用来描述来自对地球系统组成部分初始条件的不完善知识的不确定性，但模型中近似产生的确定性很大程度上取决于模型的选择。一种方便的量化这些近似效果的方法是将多个模型的输出组合起来，这些模型是独立开发、初始化和运行的。为此，C3S提供了多系统季节性预报服务，该服务收集、处理和结合了在多个欧洲国家的预报中心开发和运行的最先进的季节性预报系统的数据，以实现用户相关的应用。C3S季节性多系统的组成以及支撑该服务的数据库的全部内容在文档中进行了描述。数据被分组在几个目录条目（CDS数据集）中，目前按变量的类型（单级或多级，在压力面上）和后处理的水平（原始时间分辨率的数据、时间聚合的处理和与偏差调整相关的后处理）定义。下表中列出了此数据集中可用的变量。该数据包括从本条目发布开始，每月实时创建的预报。