Treatment of model bias in coupled atmosphere-ocean data assimilation

大气-海洋耦合资料同化模型偏差的处理

• 批准号: NE/J005835/1
• 负责人: Amos Lawless
• 金额: $ 45.28万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FJ005835%2F1
• 关键词: Treatment; model; bias; coupled; atmosphere

It is expected that the change in climate over the next century is likely to lead to many more extreme weather events, which will have significant impacts on society. In order to be able to plan for societal developments policy makers need to understand the likely effects of climate change over the coming decade. This information would be useful in planning projects designed to alleviate the effects of climate change, such as flood defences, as well as for more general projects, such as deciding where to build new housing. Scientists are currently developing methods to predict general weather phenomena over time scales of several years using computer simulations of the atmosphere and ocean. However, whereas many advances have been made in recent years in forecasting on time scales of days to weeks, the science of forecasting on much longer time scales is still in its infancy.Recent developments in this area suggest that certain parts of the climate system may be predictable on time scales of several years if we can know more accurately the current state of the atmosphere and ocean throughout the world. Data assimilation is the science of combining observations of the atmosphere or ocean with computer simulations in order to be able to determine more accurately the current conditions and so produce a better forecast. It has been widely used in both weather forecasting and ocean forecasting for many years. However in developing predictions on seasonal to inter-annual time scales we need to simulate the evolution of the atmosphere and ocean together. Determining the current atmospheric and ocean states together is made more difficult in particular by two factors. One is that the atmosphere and ocean evolve on very different time scales and this is not very well handled by current methods of data assimilation. The other factor is that the computer models inevitably contain errors, due to our imperfect knowledge, and these errors are exacerbated when we treat the two systems together. In this project we will develop new data assimilation methods to determine simultaneously the state of the atmosphere and oceans using observed data, taking account of both the different time scales in the two systems and of the unknown errors in the computer models. The direct involvement of the European Centre for Medium-range Weather Forecasts in the project will allow a transfer of knowledge to operational practice.

预计下一世纪气候的变化可能会导致更多的极端天气事件，这将对社会产生重大影响。为了能够为社会发展计划，政策制定者需要了解未来十年的气候变化的可能影响。这些信息对于旨在减轻气候变化的影响的计划项目将很有用，例如防洪以及更一般的项目，例如决定在哪里建造新住房。科学家目前正在开发使用大气和海洋的计算机模拟来预测几年时间尺度的一般天气现象的方法。但是，尽管近年来在预测数天到几周的时间尺度上已经取得了许多进步，但在更长的时间尺度上进行预测的科学仍处于起步阶段。该领域的发展表明，如果我们可以在整个世界上更准确地了解气候系统的某些部分，那么气候系统的某些部分可以预测几年的时间尺度。数据同化是将大气或海洋观察与计算机模拟相结合的科学，以便能够更准确地确定当前状况并产生更好的预测。多年来，它在天气预报和海洋预测中已被广泛使用。但是，在制定对季节到青年时间尺度的预测，我们需要一起模拟大气和海洋的演变。确定当前的大气和海洋状态，尤其是两个因素变得更加困难。一个是，大气和海洋在截然不同的时间尺度上演变，这不是当前的数据同化方法很好地处理。另一个因素是，由于我们的知识不完美，计算机模型不可避免地包含错误，并且当我们一起处理两个系统时，这些错误会加剧。在该项目中，我们将开发新的数据同化方法，以使用观察到的数据同时确定大气和海洋的状态，并考虑到两个系统中的两个不同时间尺度以及计算机模型中未知错误的不同时间尺度。欧洲中等天气预报中心的直接参与该项目将使知识转移到运营实践。