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.

预计下一个世纪的气候变化很可能导致更多的极端天气事件，这将对社会产生重大影响。为了能够规划社会发展，决策者需要了解气候变化在未来十年可能产生的影响。这一信息将有助于规划旨在减轻气候变化影响的项目，如防洪工程，以及更一般的项目，如决定在何处建造新住房。科学家们目前正在开发利用计算机模拟大气和海洋来预测几年时间尺度上的一般天气现象的方法。然而，尽管近年来在几天到几周的时间尺度上的预测取得了许多进展，但在更长时间尺度上的预测科学仍处于起步阶段，这一领域的最新发展表明，如果我们能够更准确地了解全球大气和海洋的现状，气候系统的某些部分可能在几年的时间尺度上是可以预测的。数据同化是将大气或海洋观测与计算机模拟相结合的科学，以便能够更准确地确定当前条件，从而产生更好的预测。多年来，它已广泛用于天气预报和海洋预报。然而，在发展季节到年际时间尺度的预测时，我们需要同时模拟大气和海洋的演变。特别是由于两个因素，使确定当前大气和海洋状态变得更加困难。一个是大气和海洋在非常不同的时间尺度上演变，这不是很好地处理目前的数据同化方法。另一个因素是，由于我们的知识不完善，计算机模型不可避免地包含错误，当我们将两个系统放在一起时，这些错误会加剧。在这个项目中，我们将开发新的数据同化方法，利用观测数据同时确定大气和海洋的状态，同时考虑到两个系统的不同时间尺度和计算机模型中的未知误差。欧洲中期天气预报中心直接参与该项目，将有助于将知识转化为业务实践。