Coupled Model Errors in the Tropical Atlantic in CMIP5 and their impact on the reliability of climate projections.

在IPCC第四次评估报告中广泛使用的第三次耦合模式比对项目（CMIP3）的许多气候模式在热带大西洋具有共同的偏差。这些偏倚的特征是热带大西洋东部的海表温度偏暖，热带大西洋西部和赤道两侧的海表温度偏冷。这些海温偏倚与过于强烈的东北信风和过于微弱的赤道东风气流有关。海温对包括季风在内的区域天气系统起着重要的决定作用，但也部分受区域环流的影响。通过大气的大尺度环流，全球某一地区的海面温度也可以远程影响天气。海温偏倚和风偏倚之间的关系与它们的发展是一致的，它们是通过模式中大气和海洋分量的偏倚之间的耦合而相互加强的，因此很难确定偏倚的原始来源。在本项目中，我们建议对最新模式比对项目（CMIP5）的模型质量进行评估，该项目将成为IPCC第五次评估报告的主要信息来源。我们将使用对CMIP5档案的贡献，这些档案已经从大气和海洋的观测状态初始化并运行了10年，以检查这些错误是如何发展的，并确定那些导致偏差发展的过程，为未来的模式开发提供信息。我们还将确定这些偏差对我们近期（未来10 -20年）气候预测信心的影响，特别是在热带大西洋周围地区（如非洲和亚马逊），以及全球。

Many of the climate models that contributed to the third coupled model intercomparison project (CMIP3) which were extensively used in the IPCC 4th Assessment Report share common biases in the tropical Atlantic. These biases are characterised by a warm sea-surface temperature (SST) bias in the Eastern Tropical Atlantic and cold SST biases in the western tropical Atlantic and either side of the equator. These SST biases are associated with too strong north-easterly trade winds and too weak easterly flow on the equator. SSTs play a large role in determining regional weather systems including the monsoons, but are also partly determined by the regional circulation. Through the large-scale circulation of the atmosphere, sea surface temperatures in one region of the globe can also influence the weather remotely. The relationship between the SST biases and the wind biases is consistent with their development through a coupling between biases in the atmospheric and oceanic components of the model which reinforce each other, and as such it is difficult to determine the original source of the bias. In this project we propose to assess the quality of the models contributing to the latest model intercomparison project (CMIP5) which will be the main source of information to inform the 5th Assessment Report of the IPCC. We will use the contributions to the CMIP5 archive which have been initialized from the observed state of the atmosphere and ocean and run for 10 years to examine how these errors develop and identify those processes which contribute to the development of the biases to inform future model development. We will also identify the impact these biases have on our confidence in near term (next 10 -20 years) prediction of the climate, in particular in the regions around the tropical Atlantic (e.g. Africa and the Amazon) but also globally.