European Eddy-RIch ESMs (EERIE)

涡流丰富的地球系统模型(EERIE)将开发新一代地球系统模型(ESM)，这些模型能够明确表示地球系统中一个至关重要但尚未探索的区域--海洋中尺度。利用最新的科学和技术进步，EERIE将大大提高这类ESM的能力，以忠实地反映全球气候百年尺度的演变，特别是其变异性、极端和临界点在海洋中尺度的影响下可能如何展开。模型的改进包括新的动力核心，新的组件(特别是海冰)，尺度感知的参数化和机器学习(ML)的补充使用。与这一雄心相关的技术挑战非常高。Eerie的目标是实现每天高达5个模拟年(5个SYPD)的模拟速度，并有效利用欧洲目前可用的亿级前超级计算机(将功耗降低50%)。EERIE将利用的技术解决方案是使用降低的精度、图形处理器、ML和减少的I/O。除了模型改进之外，EERIE还将开发适合中尺度的创新实验模拟协议，这将代表全球气候模型界首创，为下一次IPCC做准备。EERIE将产生有用和可用的气候信息，这些信息将有助于政府间气候变化专门委员会等国家和国际气候变化评估；它将通过多学科仿真将模型变异性和极端情况纳入综合评估模型(IAM)；它将提供关于气候临界点和疾病爆发后果的故事线方法；没有项目摘要。

Eddy Rich Earth System Models (EERIE) will develop a new generation of Earth System Models (ESMs) that are capable of explicitly representing a crucially important, yet unexplored regime of the Earth system, the ocean mesoscale. Leveraging the latest advances in science and technology, EERIE will substantially improve the ability of such ESMs to faithfully represent the centennial-scale evolution of the global climate, especially its variability, extremes and how tipping points may unfold under the influence of the ocean mesoscale. Model improvements include new dynamical cores, new components (particularly sea ice), scale-aware parametrization and the complementary use of Machine Learning (ML) The technological challenge associated with this ambition is very high. EERIE’s goal is to achieve a simulation speed of up to 5 simulated years per day (5 SYPDs) and to make efficient use (reduction in power consumption by 50%) of the pre-exascale supercomputers now available in Europe. The technological solutions that are to be leveraged in EERIE are the use of reduced precision, GPUs, ML and reduced I/O. Alongside model improvements, EERIE will develop innovative experimental simulation protocols that are suitable for the mesoscale, to be pioneered on behalf of the global climate modelling community, in preparation for the next IPCC. EERIE will produce useful and usable climate information that will contribute to national and international climate change assessments such as IPCC; it will incorporate model variability and extremes within an Integrated Assessment Model (IAM) via a MLemulato;, and it will deliver storyline approaches to the consequences of climate tipping points and disease outbreaksno project summary.