Climate Model Initialization and Improvement using Particle Filters CLIMIP

使用粒子过滤器 CLIMIP 进行气候模型初始化和改进

• 批准号: NE/J005878/1
• 负责人: Peter Jan Van Leeuwen
• 金额: $ 51.1万
• 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%2FJ005878%2F1
• 关键词: Climate; Model; Initialization; Improvement; using

Policymakers require predictions of regional and local climate for the near-term (the next 1-30 years) in order to plan effective adaptation strategies. Such predictions exist, but are subject to considerable uncertainties, firstly because our global climate models (GCMs) are imperfect, and because of the internal variability of climate, which can mask or enhance the changes due to external forcings, especially on regional scales (e.g. Hawkins and Sutton, 2009). Progress in climate science to improve GCMs and reduce uncertainty, and to use them more effectively to predict some of the internal variability component would therefore be extremely valuable. This project addresses both of these areas by providing a novel new method for initialising the GCMs, comparing that method with the state-of-the-art DePreSys system, and by identifying the processes responsible for model biases, and hence suggesting methods for GCM improvement.

政策制定者需要对近期（未来 1-30 年）区域和当地气候进行预测，以便规划有效的适应策略。这种预测是存在的，但存在相当大的不确定性，首先是因为我们的全球气候模型（GCM）不完善，而且气候的内部变化可能掩盖或增强外部强迫造成的变化，特别是在区域尺度上（例如Hawkins和Sutton，2009）。因此，气候科学的进步对于改进 GCM 和减少不确定性以及更有效地利用它们来预测某些内部变率成分将非常有价值。该项目通过提供一种初始化 GCM 的新颖方法、将该方法与最先进的 DePreSys 系统进行比较、识别导致模型偏差的过程来解决这两个领域，从而提出 GCM 改进方法。

项目成果

A simple method for integrating a complex model into an ensemble data assimilation system using MPI

• DOI: 10.1016/j.envsoft.2015.02.003
• 发表时间: 2015-06
• 期刊: Environ. Model. Softw.
• 作者: P. Browne;Simon Wilson

A comparison of the equivalent weights particle filter and the local ensemble transform Kalman filter in application to the barotropic vorticity equation

• DOI: 10.3402/tellusa.v68.30466
• 发表时间: 2016-01-01
• 期刊: TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
• 影响因子: 2
• 作者: Browne, Philip A.

Representation errors and retrievals in linear and nonlinear data assimilation

• DOI: 10.1002/qj.2464
• 发表时间: 2015-07
• 期刊: Quarterly Journal of the Royal Meteorological Society
• 影响因子: 8.9
• 作者: P. V. van Leeuwen

Twin experiments with the equivalent weights particle filter and HadCM3

• DOI: 10.1002/qj.2621
• 发表时间: 2015-10-01
• 期刊: QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
• 影响因子: 8.9
• 作者: Browne, P. A.;van Leeuwen, P. J.
• 通讯作者: van Leeuwen, P. J.

A systematic method of parameterisation estimation using data assimilation

• DOI: 10.3402/tellusa.v68.29012
• 发表时间: 2016-01-01
• 期刊: TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
• 影响因子: 2
• 作者: Lang, Matthew;Van Leeuwen, Peter Jan;Browne, Philip
• 通讯作者: Browne, Philip