Climate Model Initialization and Improvement using Particle Filters CLIMIP

决策者需要对近期（今后1-30年）的区域和地方气候进行预测，以便规划有效的适应战略。这类预测是存在的，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不完善，而且气候的内部变异性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和萨顿，2009年）。因此，在气候科学方面取得进展，改进大气环流模型，减少不确定性，并更有效地利用这些模型来预测某些内部变率成分，将是极为宝贵的。该项目通过提供一种新的初始化GCM的新方法来解决这两个领域，将该方法与最先进的DePreSys系统进行比较，并通过确定负责模型偏差的过程，从而提出GCM改进的方法。

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.