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

为第三个耦合模式相互比较项目（CMIP 3）做出贡献的许多气候模式在气候变化专门委员会第四次评估报告中得到广泛使用，但在热带大西洋中存在共同的偏差。这些偏差的特点是热带大西洋东部的暖海表面温度（SST）偏差和热带大西洋西部和赤道两侧的冷SST偏差。这些SST偏差与赤道上太强的东北信风和太弱的东风有关。SST在决定包括季风在内的区域天气系统方面发挥着重要作用，但也部分取决于区域环流。通过大规模的大气环流，地球仪某一区域的海洋表面温度也可以远程影响天气。SST偏差和风偏差之间的关系是一致的，它们的发展是通过相互加强的模式中的大气和海洋分量之间的偏差的耦合，因此，很难确定偏差的原始来源。在本项目中，我们建议评估为最新的模型相互比较项目（CMIP 5）做出贡献的模型的质量，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CMIP 5档案的贡献，这些档案已从大气和海洋的观测状态初始化，并运行了10年，以研究这些误差是如何发展的，并确定那些有助于偏差发展的过程，为未来的模型开发提供信息。我们还将确定这些偏差对我们在短期（未来10 - 20年）气候预测中的信心的影响，特别是在热带大西洋周围地区（例如非洲和亚马逊）以及全球范围内。

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.