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CAS FGOALS-f3-L Model Datasets for CMIP6 Historical Atmospheric Model Intercomparison Project Simulation


doi: 10.1007/s00376-019-9027-8

  • 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.
    摘要: 中国科学院大气物理研究所开发的CAS FGOALS-f3-L 气候系统模式参加了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)。本文介绍了FGOALS-f3-L模式参加DECK试验中的全球大气环流模式(AMIP)模拟试验数据,详细说明了CAS FGOALS-f3-L模式的动力框架、物理过程、模式试验设计、数据信息以及初步评估结果。CAS FGOALS-f3-L模式采用时间滞后法生成了不同初始场的三个集合成员,并提供1979-2014年全球尺度的模拟结果。模式输出37个变量,包含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CAS FGOALS-f3-L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模式能够很好地模拟出大尺度全球大气环流和降水的基本特征,并能够很好地模拟出降水和850hPa风的MJO传播特征、台风的活动以及极端降水的发生频次特征。该数据集将为CMIP计划模式发展评估的连续性作出贡献。
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Manuscript received: 09 February 2019
Manuscript revised: 30 March 2019
Manuscript accepted: 19 April 2019
通讯作者: 陈斌, bchen63@163.com
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CAS FGOALS-f3-L Model Datasets for CMIP6 Historical Atmospheric Model Intercomparison Project Simulation

    Corresponding author: Qing BAO*, baoqing@mail.iap.ac.cn
  • 1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2. Chinese Academy of Sciences Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China
  • 3. University of Chinese Academy of Sciences, Beijing 100049, China
  • 4. Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540-6649, USA
  • 5. School of Atmospheric Sciences/Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225, China
  • 6. State Key Laboratory of Earth Surface Processes and Resource Ecology/Academy of Disaster Reduction and Emergency Management Ministry of Civil Affairs and Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • 7. School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

Abstract: 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.

摘要: 中国科学院大气物理研究所开发的CAS FGOALS-f3-L 气候系统模式参加了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)。本文介绍了FGOALS-f3-L模式参加DECK试验中的全球大气环流模式(AMIP)模拟试验数据,详细说明了CAS FGOALS-f3-L模式的动力框架、物理过程、模式试验设计、数据信息以及初步评估结果。CAS FGOALS-f3-L模式采用时间滞后法生成了不同初始场的三个集合成员,并提供1979-2014年全球尺度的模拟结果。模式输出37个变量,包含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CAS FGOALS-f3-L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模式能够很好地模拟出大尺度全球大气环流和降水的基本特征,并能够很好地模拟出降水和850hPa风的MJO传播特征、台风的活动以及极端降水的发生频次特征。该数据集将为CMIP计划模式发展评估的连续性作出贡献。

1. Introduction
  • The Coupled Model Intercomparison Project (CMIP) has been an essential platform to better understand past, present and future climate change arising from natural, unforced variability or in response to changes in radiative forcings in a multimodel context (Eyring et al., 2016). In recent years, phase 6 of CMIP (CMIP6) was launched with a new and more federated structure that has many updates to the experiments in CMIP5. The experiments in CMIP6 are divided into two groups. One is the Diagnostic, Evaluation and Characterization of Klima (DECK) and CMIP6 historical simulations. The other is the Endorsed Model Intercomparison Projects (MIPs). The DECK and CMIP historical simulations (1850-near present) group, maintains continuity and helps to retain the basic document characteristics of models across different phases of CMIP. The Endorsed MIPs group addresses a large range of specific questions and fills the scientific gaps in previous CMIP phases.

    The Atmospheric Model Intercomparison Project(AMIP) simulation (Gates, 1992; Gates et al., 1999) is the first experiment designed in DECK. The AMIP experiment has been routinely carried out by modeling centers to evaluate their atmospheric models for its simplicity in methodology over the last three decades. The aim of the simulation is to analyze and evaluate the atmosphere and land in the climate system when they are constrained by observed sea surface temperatures (SSTs) and sea-ice concentrations. The systematic model errors can be identified by comparing the simulations to the observed atmosphere and land states in statistical ways. The simulation can also be useful for understanding climate variability and many aspects of historical climate changes for the climate science community.

    The low-resolution version of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System model, finite-volume version 3 (CAS FGOALS-f3-L) climate system model was developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), CAS (Bao et al., 2019). The model completed the AMIP simulations in late 2018, and the model outputs were prepared for release after a series of postprocesses. To provide a description of the AMIP model outputs and the relevant essential model configurations and experimental methods for a variety of users, we document detailed descriptions of the AMIP simulation by CAS FGOALS-f3-L in this paper. Section 2 presents the model description and experimental design. Section 3 addresses the technical validation of the outputs from the CAS FGOALS-f3-L experiments. Section 4 provides usage notes.

2. Model and experiments
  • CAS FGOALS-f3-L is composed of five components: version 2.2 of the Finite-volume Atmospheric Model (FAMIL) (Zhou et al., 2015; Bao et al., 2019; Li et al., 2019), which is the new generation atmospheric general circulation model of the Spectral Atmosphere Model of LASG (SAMIL) (Wu et al., 1996; Bao et al., 2010, Bao et al., 2013) (their main differences are shown in Table 1); version 3 of the LASG/IAP Climate system Ocean Model (LICOM3) (Liu et al., 2012); version 4.0 of the Community Land Model (CLM4) (Oleson et al., 2010); version 4 of the Los Alamos sea ice model (CICE4) (Hunke and Lipscomb, 2010); and version 7 of the coupled module from the National Center for Atmospheric Research (NCAR) (http://www.cesm.ucar.edu/models/cesm1.0/cpl7/), which is used to exchange the fluxes among these components.

    The atmospheric component, FAMIL, uses a finite-volume dynamical core (Lin, 2004) on a cubed-sphere grid (Putman and Lin, 2007), with six tiles across the globe. In FAMIL, each tile contains 96 grid cells (C96). Globally, the longitudes along the equator are divided into 384 grid cells, and the latitudes are divided into 192 grid cells, which is approximately equal to a 1° horizontal resolution. In the vertical direction, the model uses hybrid coordinates over 32 layers, and the model top is at 2.16 hPa. The main physical packages include a new moisture turbulence parameterization scheme for the boundary layer (Bretherton and Park, 2009), with shallow convection updated (Wang and Zhang, 2014). The Geophysical Fluid Dynamics Laboratory version of a single-moment six-category cloud microphysics scheme (Lin et al., 1983; Harris and Lin, 2014) is adopted to predict the bulk contents of water vapor, cloud water, cloud ice, rain, snow and graupel. For the cloud fraction diagnosis, the (Xu and Randall, 1996) scheme is used, which considers not only relative humidity but also the cloud mixing ratio, thus providing a more precise cloud fraction. A convection-resolving precipitation parameterization (\copyright 2017 FAMIL Development Team) is used where, in contrast to the conventional convective parameterization, convective and stratiform precipitation are calculated explicitly. The Rapid Radiative Transfer Model for GCMs (RRTMG) (Clough et al., 2005) was introduced into the model as the main radiation transfer, which utilizes the correlated k-distribution technique to efficiently calculate the irradiance and heating rate in 14 shortwave and 16 longwave spectral intervals. Finally, a gravity wave drag scheme is also used, based on (Palmer et al., 1986).

  • Following the design of the DECK AMIP experiments (Eyring et al., 2016), we conducted three simulations, as summarized in Table 2. In these experiments, the external forcings are prescribed as their monthly mean observation values, as recommended by the CMIP6 projects: the historical global mean greenhouse gas concentrations from (Meinshausen et al., 2017); solar forcing from (Matthes et al., 2017); historical ozone concentrations from http://blogs.reading.ac.uk/ccmi/forcing-databases-in-support-of-cmip6/; and AMIP SST and Sea Ice Datasets from the program for Climate Model Diagnosis & Intercomparison (PCMDI) at https://esgf-node.llnl.gov/projects/esgf-llnl/. The aerosol mass concentrations are also prescribed and taken from the NCAR Community Atmosphere Model with Chemistry (CAM-Chem; Lamarque et al., 2012), there are five aerosol species including sulfates, sea salts, black carbon, organic carbon, and dust. The land use datasets are prescribed as their mean climate values (Hurtt et al., 2011).

    As shown in Table 2, the experiment_id and variant_label are presented to identify each experiment and the corresponding outputs (Table 3). The time-lag method is used to realize the three perturbations that were identified by the variant_label: r1i1p1f1, r2i1p1f1, and r3i1p1f1. The characteristics in r1i1p1f1 denote the realization_index, initialization_index, physics_index, and forcing_index. The three ensemble simulations share the same model physics and forcing but differ due to their different integration start dates. The first experiment (r1i1p1f1) integrates from 1 January 1970. The first nine years are considered to be the spin-up period, and the model outputs from 1979 to 2014 are provided for public users. The second experiment (r2i1p1f1) is the same as r1i1p1f1, except that the integration start date is 1 January 1971. Similarly, the start date is 1 January 1972 in the third experiment (r3i1p1f1). All simulations are forced by the same varying external forcing during the observed time listed in the last paragraph.

3. Model validation
  • The model simulations have been evaluated on various time scales. Here, we show the validation at three temporal scales: monthly, daily, and six-hourly datasets (Table 3). The precipitation is one of the most important evaluation metrics. Here, we show the global distribution of climatological annual mean observed and simulated precipitation in Fig. 1. The observations from the Global Precipitation Climatology Project (GPCP) monthly precipitation dataset are used as the reference (Adler et al., 2003). It is clear that the model can capture the large-scale precipitation features well. Both the spatial pattern of precipitation along the ITCZ and SPCZ are well reproduced. Meanwhile, the model also suffers from systematic bias: the model simulates stronger precipitation in the tropical oceans and weaker precipitation over land than the observation.

    Figure 1.  Climatological (1979-2014) annual mean precipitation (units: mm d-1) from (a) GPCP and (b) the mean of amip r1i1p1f1, r2i1p1f1, and r3i1p1f1.

    The simulation of the Madden-Julian Oscillation (MJO) is of interest in current climate models and has remained a great challenge in recent years (Jiang et al., 2015). Here, we present the model skill in capturing the MJO based on daily precipitation and 850 hPa winds. The observed daily precipitation from GPCP (Huffman et al., 2001) and the wind field from ERA-Interim (Dee et al., 2011) are used as reference observations. Using a 20-100-day band-filtered component, we analyzed the zonal propagation of precipitation (colors) and 850-hPa zonal winds (contours) against precipitation in an Indian Ocean reference region (10°S-5°N, 75°-100°E) for boreal winter (Fig. 2). Here, winter is defined from November to April of the following year, following (Waliser et al., 2009). Compared with the observations, the dominant feature of MJO eastward propagations (from the Indian Ocean via the western Pacific to the International Date Line) can be simulated well in both precipitation and 850 hPa winds in the AMIP simulation. The quadrature relationship between precipitation and the 850 hPa zonal winds (U850) is reproduced well over the Indian Ocean and western Pacific Ocean in the simulation. Meanwhile, the phase speed is nearly 4-5 m s-1, and the lag of the wind anomaly behind precipitation is approximately 5-7 days in the simulation, which is also similar to the result observed in (Waliser et al., 2009). Compared with the previous version of FAMIL (Yang et al., 2012), the MJO simulation is substantially improved. Small weaknesses are also identified. The propagation of precipitation is increased by two to three days compared to the observation reaching the date line. The rainfall amplitude associated with the MJO is also slightly weaker than in the observation.

    Figure 2.  November-April lag-longitude diagram of the 10°S-10°N intraseasonal precipitation anomalies (colors) and intraseasonal 850-hPa zonal wind anomalies (contours) correlated with intraseasonal precipitation over the Indian Ocean reference region (10°S-5°N, 75°-100°E) for (a) observations and (b) the mean of amip r1i1p1f1, r2i1p1f1, and r3i1p1f1.

    Tropical cyclones (TCs), as one of the most drastic phenomena in the world, have considerable impacts on human life. TC forecasting is still a challenge in that most models are unable to predict the tracks very well (Xiang et al., 2015). We evaluate the simulations of TC tracks in AMIP r1i1p1f1 based on the six-hourly datasets in Fig. 3. The observed TC tracks (Fig. 3a) are derived from the International Best Track Archive for Climate Stewardship (IBTrACS) (version v03r09) dataset (Knapp et al., 2010), which shows that TCs are active in subtropical oceans in both hemispheres, except for the southeastern Pacific Ocean and the southern Atlantic Ocean. TCs were classified into seven categories according to the Saffir-Simpson (SS) scale (Simpson and Saffir, 1974), as shown in different colors in Fig. 3. Based on the six-hourly dataset in AMIP r1i1p1f1, the model could successfully capture the global pattern of the tropical storm (TS) tracks (green lines) (Fig. 3b), except that the model underestimates the TS tracks in the eastern Pacific and northern Atlantic Ocean while producing unrealistic tracks over the southern Atlantic. Category 4 and 5 TCs are also underestimated in the model. These results suggest that the model can capture TC tracks, but the intensity is slightly weaker, and the six-hourly datasets are quite reliable for conducting TC research.

    Figure 3.  TC tracks (lines) and intensities (colors) (a) from a 36-year segment (1979-2014) of IBTrACS data and (b) from the simulation of AMIP r1i1p1f1, which are detected by using an objective feature-tracking approach at the C96 resolution (approximately 100 km) from 1979-2014. Only those TCs with a lifetime exceeding three days are shown. The TCs in both the simulation of AMIP r1i1p1f1 and the observation of IBTrACS are grouped into seven categories in accordance with the modified Saffir-Simpson scale, but only TCs stronger than TSs are shown.

    Realistic reproduction of historical extreme precipitation has been challenging for both reanalysis and GCM simulations (He et al., 2019). We evaluate the simulations of extreme precipitation over the tropics (20°S-20°N) in the AMIP r1i1p1f1 daily outputs. Tropical Rainfall Measuring Mission (TRMM)-3B42 data are used as reference observations (Huffman et al., 2007) and were interpolated at the same resolution as AMIP r1i1p1f1 by the nearest-neighbor interpolation method (Accadia et al., 2003). The frequency of precipitation was plotted against the daily precipitation rate at a 1 mm d-1 interval. For the extreme precipitation, the frequency-intensity distribution (Fig. 4) in CAS FGOALS-f3-L is extended up to 350 mm d-1, which shows that similar characteristics manifested in the TRMM data. The frequency-intensity distribution in the model is also comparable under 50 mm d-1 with the TRMM data, while it is slightly overestimated above 50 mm d-1. These results suggest that the model can simulate enough extreme precipitation over tropical regions, but the frequency of extreme precipitation is slightly overestimated.

    Figure 4.  Annual rainfall frequency-intensity distribution in TRMM-3B42 (black line) and CAS FGOALS-f3-L AMIP r1i1p1f1 (blue line) from 1998-2010. The domain is the tropical region (20°S-20°N).

4. Usage notes
  • The original atmospheric model grid is in the cube-sphere grid system with the resolution of C96, which has six tiles and is irregular in the horizonal direction. We merge and interpolate the tiles to a nominal resolution of 1° on a global latitude-longitude grid scaled by one-order conservation interpolation, as required by CMIP6, for public use. For the users who want to calculate pressure at model layers, we provide hybrid level "A" coefficient at mid-point levels (hyam), hybrid level "B" coefficient at mid-point levels (hybm), hybrid level "A" coefficient on the interfaces (hyai), and hybrid level "B" coefficient on the interfaces (hybi) in Table 4. Then, the pressures can be derived from the following expression: \begin{equation} P_{(i,j,k)}=A_{k}P_{0}+B_{k}Ps_{(i,j)} \nonumber, \end{equation} where P(i,j,k) denotes the desired pressure at the model mid-point level or interfaces, Ak denotes hyam or hyai, P0 denotes 1000 hPa, Bk denotes hybm, hybi, and Ps(i,j) denotes the surface pressure. The (i,j,k) denotes the longitude index, latitude index, and vertical layer index, respectively.

    The format of datasets is the version 4 of Network Common Data Form (NetCDF), which can be easily read and written by professional common software such as Climate Data Operators (https://www.unidata.ucar.edu/software/netcdf/workshops/2012/third_party/CDO.html), NetCDF Operator (http://nco.sourceforge.net), NCAR Command Language (http://www.ncl.ucar.edu), and Python (https://www.python.org).

  • The data that support the findings of this study are available from https://esgf-node.llnl.gov/projects/cmip6/.

  • No potential conflict of interest was reported by the authors.

    Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

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