Finite-volume Atmospheric Model of the IAP/LASG

FAMIL1

The Finite-volume Atmospheric Model of the Institute of Atmospheric Physics (IAP)/State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) (FAMIL), Chinese Academy of Sciences (CAS), is one of the newest atmospheric components of the Flexible Global Ocean-Atmosphere-Land System (FGOALS) model. The previous version of FAMIL is a spectral atmospheric general circulation model (AGCM) called the Spectral Atmospheric Model of the IAP/LASG (SAMIL). SAMIL has been developed from a low-resolution model (R15, approximately 400 km, in horizontal grids and nine vertical layers) to an intermediate-resolution model (R42, approximately 200 km, in horizontal grids and 26 vertical layers) [Wu et al., 1996, 2003; Wang et al., 2004; Bao et al., 2010]. As with previous versions of the general circulation models (GCMs) developed by the LASG/IAP, the latest version of its AGCM, FAMIL, is also aimed at taking active roles in most of the modeling intercomparison projects, including the Aqua Planet Experiment (APE), Atmospheric Model Intercomparison Project (AMIP), Coupled Model Intercomparison Project (CMIP), Cloud Feedback Model Intercomparison Project (CFMIP), and the scenario runs of the Intergovernmental Panel on Climate Change (IPCC).

The first version of FAMIL (FAMIL1) adopts the finite-volume algorithm in the module of the dynamic core; this algorithm is calculated on a cubed-sphere grid system, thus avoiding the pole issue inherent in longitude-latitude grid systems [Lin, 2004; Putman and Lin, 2007]. A flux-form semi-Lagrangian transport scheme is used to calculate the advection terms in FAMIL1, making it both stable and conservative [Lin and Rood, 1996; Wang et al., 2013]. The model’s horizontal resolution is flexible and can be changed from 200 to 25 km while the size of the grid cell varies as a factor from 0.81 to 1.16. The number of vertical levels in FAMIL1 has been increased to 32 (from 26 in SAMIL2), in which there are eight levels in the planetary boundary layer and 20 levels in the troposphere. The uppermost level in FAMIL1 has a pressure of 2.16 hPa, a height of about 40 km for a surface pressure of 1013.25 hPa.

The following physical parameterizations of FAMIL1 are the same as those in SAMIL: the cumulus convective parameterization is based on the bulk mass-flux framework developed by Tiedtke [1989], in which three types of convections—penetrative convection in connection with large-scale convergent flow, shallow convections in suppressed conditions such as trade wind cumuli, and middle convection such as extratropical organized convection—are uniformly treated. In addition to the default trigging function and closure assumption in deep convection, two other variants have also been added, including those based on convective available potential energy (CAPE) [Nordeng, 1994], and the dynamic CAPE in which large-scale dynamics is accounted for [Xie and Zhang, 2000; Zhang, 2002; Wang and Zhang, 2013]. However, the dynamic CAPE so far on is only for single-column experiments, through which the sensitivity of triggering and closure assumptions can be easily studied. For FAMIL1 and SAMIL2, the default choice is still the one proposed by Nordeng [1994]. The planetary boundary layer scheme of the model is a "nonlocal" first-order closure schemes determining an eddy-diffusivity profile based on a diagnosed boundary layer height and a turbulent velocity scale. It also incorporates nonlocal (vertical) transport effects for heat and moisture. This scheme represents the effects of dry convective plumes whose vertical scale is the depth of the boundary layer. Within this scheme, the boundary layer depth is calculated explicitly [Holtslag and Boville, 1993]. The gravity wave drag scheme considers only the topographic effect [Palmer et al., 1986].

In order to improve the performance of the simulated energy and water balance, radiation, cloud microphysics, and some parameters in the convection schemes have been updated in FAMIL1, which are majorly different from SAMIL. First, the latest version of the Rapid Radiative Transfer Model for GCMs (RRTMG) [Clough et al., 2005] (http://rtweb.aer.com/rrtm_frame.html) replaced the Sun-Edwards-Slingo radiation scheme [Edwards and Slingo, 1996; Sun, 2011] in SAMIL. RRTMG utilizes the correlated k-distribution technique to efficiently calculate the irradiance and heating rate in 14 shortwave and 16 longwave spectral intervals. The Monte-Carlo Independent Column Approximation is included into RRTMG to treat subgrid cloud overlap [Pincus et al., 2003]. Besides, prescribed aerosol fields are taken from the National Center for Atmospheric Research (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. In the original NCAR aerosol data set, only the bulk masses with lognormal distributions are calculated for black carbon, organic carbon, and sulfate. Four size bins are used for sea-salt and dust aerosols. The details about aerosol size parameters are provided by Lamarque et al. [2010], which are the standard forcings recommended by CMIP5. The aerosol data sets have a 1.9o x 2.5o horizontal resolution and a monthly temporal resolution with a seasonal cycle. Aerosol optical properties for each species follow the treatments in the version of the RRTMG radiation scheme used in NCAR CAM5 [Ghan and Zaveri, 2007; Liu et al., 2012]. Different aerosol species are externally mixed in the radiation calculation. Then, to improve the performance of water balance, FAMIL1 implements a single moment cloud microphysics scheme, the same as that used in the Geophysical Fluid Dynamics Laboratory High Resolution Atmosphere Model (GFDL HiRAM) [Harris and Lin, 2014], to predict bulk contents of cloud water, rain, snow, ice crystals, and graupel/hail, instead of simple large-scale condensation processes as used in SAMIL2. The algorithms of this cloud microphysics scheme were initially based on Lin et al. [1983], but many key elements have been changed/improved based on several other publications [Rutledge and Hobbs, 1984; Dudhia, 1989; Fowler et al., 1996; Hong et al., 2004]. In the parameterization, cloud condensation nuclei (CCN) are prescribed, and only the land-ocean difference of CCN is considered [Klein and Jakob, 1999]. A linear subgrid vertical distribution of cloud water and cloud ice is assumed following Lin et al. [1994]. With this cloud microphysics scheme, detrained cloud water and cloud ice can be transformed to other forms of water, and large-scale precipitation is explicitly calculated, and more water phases can be considered for precisely simulating the water distribution and variability. The calculation of cloud after cloud microphysics processes employs the Xu and Randall [1996] scheme, which considers not only relative humidity but also the cloud mixing ratio, thus providing a more precise cloud diagnosis. Finally, all physical parameterizations are corrected to be energy and water conserved in every model column at every time step.


FAMIL2

As in FAMIL1 [Zhou et al., 2015], FAMIL2 uses a finite-volume dynamical core [Lin, 2004], and combines this with a flux-form semi-Lagrangian advection scheme [Wang et al., 2013] on a cubed-sphere grid system [Putman et al., 2007]. The horizontal resolutions of FAMIL2 can vary from C48 (approximately 200 km) to C1536 (approximately 6.25 km) in both AMIP-like runs and a fully coupled run. The number of vertical layers has been increased to 32 from 26, and the atmospheric layers extend from the surface to 1 hPa. The vertical spacing of FAMIL2 is the same as GFDL HiRAM [Zhao et al., 2009].

Compared with FAMIL1, FAMIL2 has replaced the planetary boundary layer scheme, which was a "nonlocal" first-order closure scheme [Holtslag and Boville, 1993], with the University of Washington moist turbulence (UWMT) parameterization [Park et al., 2009]. UWMT is a nonlocal high-order closure scheme and uses diagnosed turbulent kinetic energy to determine the eddy diffusivity in turbulence. Compared to the previous planetary boundary scheme, the UWMT scheme considers turbulence to be affected by all processes that influence the vertical structure, and the results of the UWMT scheme are insensitive to changes in the horizontal or vertical resolution. The land surface model used in FAMIL2 is CLM4.0 (version 4 of the Community Land Model; Oleson et al. 2010), which is coupled with the atmospheric component via the version 7 coupler in CESM [Craig et al., 2012]. The frequency of the coupling is 48 times per day, and the processes from the dynamic global vegetation model (DGVM) in CLM4.0 are turned off during the coupling with FAMIL2. Thus, the processes related to carbon biogeochemistry, nitrogen biogeochemistry, and the urban model are fixed in FAMIL2. As in FAMIL2, there are two horizontal resolutions of CLM4.0: 1 degree nominal resolution for the most CMIP6- endorsed MIPs and 0.25 degree nominal resolution for CMIP6 HighResMIP. Besides, a resolving convective precipitation parameterization (©2017 FAMIL Development Team) is used, which involves calculating the microphysical processes in the cumulous scheme for both deep and shallow convection, six species are considered similar to the microphysics scheme [Lin et al., 1983; Harris and Lin, 2014]. Because of the quick phase changes within the convective cloud, a sub-timestep of 150 s is used to calculate the microphysical processes.


FGOALS-f2 & FGOALS-f3

The Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System Model, finite volume version 2&3 (FGOALS-f2, FGOALS-f3), are two coupled global climate models developed by 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), Beijing, China [Bao et al., 2019]. The atmospheric component of FGOALS-f2&3 both is FAMIL2 [He et al., 2019; Bao et al., 2019; Li et al., 2019]. The only difference between FGOALS-f2 and FGOALS-f3 is the ocean component: Parallel Ocean Program version 2 [Smith et al., 2010] is used in FGOALS-f2, while LASG/IAP climate system ocean model version 3 (LICOM3) [Liu et al., 2013] is used in FGOALS-f3, All other components (e.g., Community Land Model version 4 [Oleson et al., 2010], Community Ice CodE version 4 [Hunke et al., 2010]) are also upgraded from FGOALS-s2 [Bao et al., 2013]. A relaxation-based data assimilation system has been developed for FGOALS-f2 to assimilate the JRA55 reanalysis data [Kobayashi et al., 2015; Harada et al., 2016] and the GODAS reanalysis data [Behringer and Xue, 2004]. Since mid-2017, FGOALS-f2 S2S prediction system has been operationally used in both the National Climate Center of China Meteorological Administration and The National Marine Environmental Forecasting Center of China [Bao et al., 2019]. As for participating CMIP6, FGOALS-f3-L is a standard resolution version designed for DECK with approximately 100km in FAMIL2, while FGOALS-f3-H is a hi-resolution version designed for HighResMIP with approximately 25km in FAMIL2 [Haarsma et al., 2016; Bao et al., 2019].


Publication:

2020

  • Sheng, C., and Coauthors, 2020: Characteristics of the potential vorticity and its budget in the surface layer over the Tibetan plateau. International Journal of Climatology, n/a.

  • Zheng, W., and Coauthors, 2020: CAS-FGOALS Datasets for the Two Interglacial Epochs of the Holocene and the Last Interglacial in PMIP4. Advances in Atmospheric Sciences.

  • Guo, Y., and Coauthors, 2020: Overview of the CMIP6 Historical Experiment Datasets with the Climate System Model CAS FGOALS-f3-L. Advances in Atmospheric Sciences.

  • Guo, Y., and Coauthors, 2020: Simulation and Improvements of Oceanic Circulation and Sea Ice by the Coupled Climate System Model FGOALS-f3-L. Advances in Atmospheric Sciences.

  • Yiqiong, T., H. E. Bian, B. A. O. Qing, L. I. U. Yimin, L. I. Jinxiao, and W. U. Guoxiong, 2020: The climate variability in global land precipitation in FGOALS-f3-L: A comparison between GMMIP and historical simulations. Atmospheric and Oceanic Science Letters, 1-9.

  • He, B., and Coauthors, 2020: CAS FGOALS-f3-L model dataset descriptions for CMIP6 DECK experiments. Atmospheric and Oceanic Science Letters, 1-7.

2019

  • He, B., and Coauthors, 2019: CAS FGOALS-f3-L Model Datasets for CMIP6 GMMIP Tier-1 and Tier-3 Experiments. Advances in Atmospheric Sciences, 37, 18-28.

  • Zhuang, M., and A. Duan, 2019: Revisiting the Cross-Equatorial Flows and Asian Summer Monsoon Precipitation Associated with the Maritime Continent. Journal of Climate, 32, 6803-6821.

  • Hao, Y., B. Liu, C. Zhu, and B. He, 2019: Asymmetry in the dominant co-variation mode of boreal summer monsoon rainfall regulated by the ENSO evolution. Climate Dynamics, 53, 6379-6396.

  • He, B., and Coauthors, 2019: CAS FGOALS-f3-L Model Datasets for CMIP6 Historical Atmospheric Model Intercomparison Project Simulation. Adv Atmos Sci, 36, 771-778.

  • Wang, L., and Coauthors, 2019: LASG Global AGCM with a Two-moment Cloud Microphysics Scheme: Energy Balance and Cloud Radiative Forcing Characteristics. Adv. Atmos. Sci. 36, 697-710.

  • Li, J. X., and Coauthors, 2019: Evaluation of FAMIL2 in Simulating the Climatology and Seasonal-to-Interannual Variability of Tropical Cyclone Characteristics. J Adv Model Earth Sy, 11, 1117-1136.

  • Wang, L., Q. Bao, J. Li, D. Wang, Y. Liu, G. Wu, and X. Wu, 2019: Comparisons of the temperature and humidity profiles of reanalysis products with shipboard GPS sounding measurements obtained during the 2018 Eastern Indian Ocean Open Cruise. Atmospheric and Oceanic Science Letters, 12, 177-183.

  • He, S., J. Yang, Q. Bao, L. Wang, and B. Wang, 2019: Fidelity of the Observational/Reanalysis Datasets and Global Climate Models in Representation of Extreme Precipitation in East China. Journal of Climate, 32, 195-212.

  • Bao, Q., and Coauthors, 2019: Outlook for El Niño and the Indian Ocean Dipole in autumn-winter 2018–2019. Chin. Sci. Bull., 64, 73.

2018

  • Yu, J., Y. Liu, T. Ma, G. Wu, 2018: The influence of surface potential vorticity density forcing over the Tibetan Plateau in the 2008 winter storm. Part Ⅱ: Numerical simulation. Acta Meteorologica Sinica, 76, 887-903. (In Chinese)

  • Wang, Z. Q., S. Yang, N. C. Lau, and A. M. Duan, 2018: Teleconnection between Summer NAO and East China Rainfall Variations: A Bridge Effect of the Tibetan Plateau. Journal of Climate, 31, 6433-6444.

  • Wong, K. C., S. F. Liu, A. G. Turner, and R. K. Schiemann, 2018: Different Asian Monsoon Rainfall Responses to Idealized Orography Sensitivity Experiments in the HadGEM3-GA6 and FGOALS-FAMIL Global Climate Models. Adv Atmos Sci, 35, 1049-1062.

  • Zhao, Y., A. M. Duan, and G. X. Wu, 2018: Interannual Variability of Late-spring Circulation and Diabatic Heating over the Tibetan Plateau Associated with Indian Ocean Forcing. Adv Atmos Sci, 35, 927-941.

  • Liu, S. F., and A. M. Duan, 2018: Impacts of the global sea surface temperature anomaly on the evolution of circulation and precipitation in East Asia on a quasi-quadrennial cycle. Clim Dyn, 51, 4077-4094.

2017

  • Li, J.-X., Q. Bao, Y.-M. Liu, and G.-X. Wu, 2017: Evaluation of the computational performance of the finite-volume atmospheric model of the IAP/LASG (FAMIL) on a high-performance computer. Atmospheric and Oceanic Science Letters, 10, 329-336.

  • Liu, S. F., and A. M. Duan, 2017: Impacts of the Leading Modes of Tropical Indian Ocean Sea Surface Temperature Anomaly on Sub-Seasonal Evolution of the Circulation and Rainfall over East Asia during Boreal Spring and Summer. Journal of Meteorological Research, 31, 171-186.

2015

  • Hu, J., and A. M. Duan, 2015: Relative contributions of the Tibetan Plateau thermal forcing and the Indian Ocean Sea surface temperature basin mode to the interannual variability of the East Asian summer monsoon. Clim Dyn, 45, 2697-2711.

  • Zhou, L. J., and Coauthors, 2015: Global energy and water balance: Characteristics from Finite-volume Atmospheric Model of the IAP/LASG (FAMIL1). J Adv Model Earth Sy, 7, 1-20.

2014

  • Yu, H.-Y., Q. Bao, L.-J. Zhou, X.-C. Wang, and Y.-M. Liu, 2014: Sensitivity of Precipitation in Aqua-Planet Experiments with an AGCM. Atmospheric and Oceanic Science Letters, 7, 1-6.

  • Zhou, L., Q. Bao, and H. Yu, 2014: High-Resolution FAMIL. Flexible Global Ocean-Atmosphere-Land System Model, T. Zhou, Y. Yu, Y. Liu, and B. Wang, Eds., Springer Berlin Heidelberg, 339-349.

2012

  • Zhou, L.-J., Y.-M. Liu, Q. Bao, H.-Y. Yu, and G.-X. Wu, 2012: Computational Performance of the High-Resolution Atmospheric Model FAMIL. Atmospheric and Oceanic Science Letters, 5, 355-359.


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