The Sunway TaihuLight supercomputer is the world's first system with a peak performance greater than 100 PFlops. In this paper, we provide a detailed introduction to the TaihuLight system. In contrast with other existing heterogeneous supercomputers, which include both CPU processors and PCIe-connected many-core accelerators (NVIDIA GPU or Intel Xeon Phi), the computing power of TaihuLight is provided by a homegrown many-core SW26010 CPU that includes both the management processing elements (MPEs) and computing processing elements (CPEs) in one chip. With 260 processing elements in one CPU, a single SW26010 provides a peak performance of over three TFlops. To alleviate the memory bandwidth bottleneck in most applications, each CPE comes with a scratch pad memory, which serves as a user-controlled cache. To support the parallelization of programs on the new many-core architecture, in addition to the basic C/C++ and Fortran compilers, the system provides a customized Sunway OpenACC tool that supports the OpenACC 2.0 syntax. This paper also reports our preliminary efforts on developing and optimizing applications on the TaihuLight system, focusing on key application domains, such as earth system modeling, ocean surface wave modeling, atomistic simulation, and phase-field simulation.

Abstract. The historical simulations of sea ice during 1979 to 2005 by the Coupled Model Intercomparison Project Phase 5 (CMIP5) are compared with satellite observations, Global Ice-Ocean Modeling and Assimilation System (GIOMAS) output data and Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) output data in this study. Forty-nine models, almost all of the CMIP5 climate models and earth system models with historical simulation, are used. For the Antarctic, multi-model ensemble mean (MME) results can give good climatology of sea ice extent (SIE), but the linear trend is incorrect. The linear trend of satellite-observed Antarctic SIE is 1.29 (±0.57) × 105 km2 decade−1; only about 1/7 CMIP5 models show increasing trends, and the linear trend of CMIP5 MME is negative with the value of −3.36 (±0.15) × 105 km2 decade−1. For the Arctic, both climatology and linear trend are better reproduced. Sea ice volume (SIV) is also evaluated in this study, and this is a first attempt to evaluate the SIV in all CMIP5 models. Compared with the GIOMAS and PIOMAS data, the SIV values in both the Antarctic and the Arctic are too small, especially for the Antarctic in spring and winter. The GIOMAS Antarctic SIV in September is 19.1 × 103 km3, while the corresponding Antarctic SIV of CMIP5 MME is 13.0 × 103 km3 (almost 32% less). The Arctic SIV of CMIP5 in April is 27.1 × 103 km3, which is also less than that from PIOMAS SIV (29.5 × 103 km3). This means that the sea ice thickness simulated in CMIP5 is too thin, although the SIE is fairly well simulated.

Both the Arctic and Antarctic sea ice extents (SIEs) from 44 coupled models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are evaluated by comparing them with observations and CMIP5 results. The CMIP6 multimodel mean can adequately reproduce the seasonal cycles of both the Arctic and Antarctic SIE. The observed Arctic September SIE declining trend (−0.82 ± 0.18 million km 2 per decade) between 1979 and 2014 is slightly underestimated in CMIP6 models (−0.70 ± 0.06 million km 2 per decade). The observed weak but significant upward trend of the Antarctic SIE is not captured, which was an issue already in the CMIP5 phase. Compared with CMIP5 models, CMIP6 models have lower intermodel spreads in SIE mean values and trends, although their SIE biases are relatively larger. The CMIP6 models did not reproduce the new summer tendencies after 2000, including the faster decline of Arctic SIE and the larger interannual variability in Antarctic SIE.