The outputs of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic, Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project (CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total, there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden-Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP. 本文介绍了中国科学院大气物理研究所开发的CAS FGOALS-f3-L 气候系统模式参加第六次国际耦合模式比较计划 (CMIP6)的DECK试验(Diagnostic, Evaluation and Characterization of Klima common experiments)中全球大气环流模式(AMIP)模拟数据, 其中包括CAS FGOALS-f3-L模式的动力框架, 物理过程简介以及模式试验设计, 数据信息以及初步评估结果. 模式采用时间滞后法产生不同初始场的三个集合成员, 并提供1979–2014年的模拟结果. 模式输出包括37个变量, 涉及3小时平均, 6小时瞬时, 日平均和月平均数据. 本文还评估了模式在不同时间尺度上的基本模拟性能. 结果表明CAS FGOALS-f3-L模式能够很好的模拟出大尺度全球大气环流和降水的基本特征, 能够很好的模拟出降水和850hPa风的MJO传播特征, 以及台风的活动和极端降水的发生频次特征. 该数据集贡献于CMIP计划在模式发展评估上的连续性.

El Niño-Southern Oscillation (ENSO) in the equatorial Pacific Ocean and the Indian Ocean Dipole (IOD) in the equatorial Indian Ocean are two major natural variabilities on seasonal and inter-annual timescales. In this study, the Flexible Global Ocean-Atmosphere-Land System Model, finite volume version 2 (FGOALS-f2), sub-seasonal to seasonal (S2S) climate prediction system, was used to make a seasonal prediction for autumn and winter 2018–2019. The FGOALS-f2 S2S prediction system was developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), and is run on China's Tianhe-2 supercomputer located at the National Supercomputer Center in Guangzhou, China. The model used in the prediction system is CAS FGOALS-f2, which is a next-generation climate system model of LASG-IAP, representing the interaction between the atmosphere, oceans, land, and sea ice. The seasonal prediction products from this system have been submitted to and used operationally by the National Climate Center of the China Meteorological Administration, as well as the National Marine Environmental Forecasting Center of China, since June 2017. The FGOALS-f2 S2S prediction system has achieved 37 a retrospective forecasts (reforecasts) covering the period 1981–2017. The reforecast experiments include 24 ensemble members, while the real-time prediction uses 35 ensemble members. The latest prediction results, in July 2018, reveal that: (1) A positive IOD will persist through autumn and winter 2018–2019, and the peak phase will be in October with an amplitude of approximately 0.4°C. Based on the 37 a reforecasts predicted from each July 20th, the one-month-lead prediction skill of the IOD is 0.82 in the IOD prediction of July, and the five-month-lead prediction skill is 0.56. (2) In the equatorial Pacific Ocean, the prediction results reveal a Moderate El Niño is under development, and Niño3.4 index values may reach approximately 1.3°C. Based on the 37 a reforecasts predicted from each July 20th, the one-month lead prediction skill of the Niño3.4 index is 0.97 in the ENSO prediction of July, and the six-month lead prediction skill is 0.83. (3) The Moderate El Niño and positive IOD may induce a weak China winter monsoon, characterized by a warm winter. For North China, the meteorological conditions are expected to have adverse effects on atmospheric diffusion; while for South China, warm and wet conditions are likely to prevail, since the lower-level jet of the Indian Ocean is predicted to strengthen. To better predict the equatorial sea surface temperature anomalies in the eastern Pacific and Indian oceans, the FGOALS-f2 S2S climate prediction team will continue to update the prediction results on the 20th of every month, releasing them (with respect to ENSO, the IOD, climate, and average monthly weather in China) on the website, and report the latest prediction results via the WeChat public platform.

The effects of horizontal resolution on the simulation of tropical cyclones were studied using the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Finite-Volume version 3 (FGOALS-f3) climate system model from the High-Resolution Model Intercomparison Project (HighResMIP) for the Coupled Model Intercomparison Project phase 6 (CMIP6). Both the low-resolution (about 100 km resolution) FGOALS-f3 model (FGOALS-f3-L) and the high-resolution (about 25 km resolution) FGOALS-f3 (FGOALS-f3-H) models were used to achieve the standard Tier 1 experiment required by HighResMIP. FGOALS-f3-L and FGOALS-f3-H have the same model parameterizations with the exactly the same parameters. The only differences between the two models are the horizontal resolution and the time step. The performance of FGOALS-f3-H and FGOALS-f3-L in simulating tropical cyclones was evaluated using observations. FGOALS-f3-H (25 km resolution) simulated more realistic distributions of the formation, movement and intensity of the climatology of tropical cyclones than FGOALS-f3-L at 100 km resolution. Although the number of tropical cyclones increased by about 50 % at the higher resolution and better matched the observed values in the peak month, both FGOALS-f3-L and FGOALS-f3-H appear to replicate the timing of the seasonal cycle of tropical cyclones. The simulated average and interannual variabilities of the number of tropical cyclones and the accumulated cyclone energy were both significantly improved from FGOALS-f3-L to FGOALS-f3-H over most of the ocean basins. The characteristics of tropical cyclones (e.g., the average lifetime, the wind–pressure relationship and the horizontal structure) were more realistic in the simulation using the high-resolution model. The possible physical linkage between the performance of the tropical cyclone simulation and the horizontal resolution were revealed by further analyses. The improvement in the response between the El Niño–Southern Oscillation and the number of tropical cyclones and the accumulated cyclone energy in FGOALS-f3 contributed to the realistic simulation of tropical cyclones. The genesis potential index and the vorticity, relative humidity, maximum potential intensity and the wind shear terms were used to diagnose the effects of resolution. We discuss the current insufficiencies and future directions of improvement for the simulation of tropical cyclones and the potential applications of the FGOALS-f3-H model in the subseasonal to seasonal prediction of tropical cyclones.

Multi-model ensemble prediction is an effective approach for improving the prediction skill short-term climate prediction and evaluating related uncertainties. Based on a combination of localized operation outputs of Chinese climate models and imported forecast data of some international operational models, the National Climate Center of the China Meteorological Administration has established the China multi-model ensemble prediction system version 1.0 (CMMEv1.0) for monthly-seasonal prediction of primary climate variability modes and climate elements. We verified the real-time forecasts of CMMEv1.0 for the 2018 flood season (June-August) starting from March 2018 and evaluated the 1991–2016 hindcasts of CMMEv1.0. The results show that CMMEv1.0 has a significantly high prediction skill for global sea surface temperature (SST) anomalies, especially for the El Niño-Southern Oscillation (EN-SO) in the tropical central-eastern Pacific. Additionally, its prediction skill for the North Atlantic SST triple (NAST) mode is high, but is relatively low for the Indian Ocean Dipole (IOD) mode. Moreover, CMMEv1.0 has high skills in predicting the western Pacific subtropical high (WPSH) and East Asian summer monsoon (EASM) in the June–July–August (JJA) season. The JJA air temperature in the CMMEv1.0 is predicted with a fairly high skill in most regions of China, while the JJA precipitation exhibits some skills only in northwestern and eastern China. For real-time forecasts in March–August 2018, CMMEv1.0 has accurately predicted the ENSO phase transition from cold to neutral in the tropical central-eastern Pacific and captures evolutions of the NAST and IOD indices in general. The system has also captured the main features of the summer WPSH and EASM indices in 2018, except that the predicted EASM is slightly weaker than the observed. Furthermore, CMMEv1.0 has also successfully predicted warmer air temperatures in northern China and captured the primary rainbelt over northern China, except that it predicted much more precipitation in the middle and lower reaches of the Yangtze River than observation.