Taiwan Earth System Model Version 1: description and evaluation of mean state

Wei-Liang Lee1, Yi-Chi Wang1, Chein-Jung Shiu1, I-chun Tsai1, Chia-Ying Tu1, Yung-Yao Lan1, Jen-Ping Chen2, Hua-Lu Pan3, and Huang-Hsiung Hsu1

1 Research Center for Environmental Changes

2 National Taiwan University

3 National Center for Environmental Protection


The Taiwan Earth System Model (TaiESM) version 1 is developed based on Community Earth System Model version 1.2.2 of National Center for Atmospheric Research. Several innovated physical and chemical parameterizations, including trigger functions for deep convection, cloud macrophysics, aerosol, and three-dimensional radiation–topography interaction, as well as a one-dimensional mixed-layer model optional for the atmosphere component, are incorporated. The precipitation variability, such as diurnal cycle and propagation of convection systems, is improved in TaiESM. TaiESM demonstrates good model stability in the 500-year preindustrial simulation in terms of the net flux at the top of the model, surface temperatures, and sea ice concentration. In the historical simulation, although the warming before 1935 is weak, TaiESM well captures the increasing trend of temperature after 1950. The current climatology of TaiESM during 1979–2005 is evaluated by observational and reanalysis datasets. Cloud amounts are too large in TaiESM, but their cloud forcing is only slightly weaker than observational data. The mean bias of the sea surface temperature is almost zero, whereas the surface air temperatures over land and sea ice regions exhibit cold biases. The overall performance of TaiESM is above average among models in Coupled Model Intercomparison Project phase 5, particularly that the bias of precipitation is smallest. However, several common discrepancies shared by most models still exist, such as the double Intertropical Convergence Zone bias in precipitation and warm bias over the Southern Ocean.

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Citation: Lee, W.-L., Y.-C. Wang, C.-J. Shiu, I.-C. Tsai, Chia-Ying Tu, Yung-Yao Lan, J.-P. Chen, H.-L. Pan, and H.-H. Hsu (2020). Taiwan Earth System Model: Description and evaluation of mean state. Geoscientific Model Development, 13, 3887-3904, https://doi.org/10.5194/gmd-13-3887-2020.

Figure 14

The root-mean-square errors (RMSEs) of upward longwave radiation at TOA in total sky and clear sky (RLUT and RLUTCS), upward shortwave radiation at TOA in total sky and clear sky (RSUT and RSUTCS), precipitation (PR), surface air temperature (TAS), geopotential height (ZG), meridional wind (VA), zonal wind (UA), and air temperature (TA) from TaiESM, CMIP5 models, and CMIP5 MME. The values of shading represent the magnitude of normalized error with respect to the median CMIP5 error. Blue/red colors indicate smaller/larger error.


Model Diagnostic


Huang-Hsiung Hsu1, Jen-Ping Chen2, Wei-Liang Lee1, Chein-Jung Shiu1, Yi-Chi Wang1, I-Chun Tsai1, Min-Hui Lo2, Chao-An Chen1, and Yung-Yao Lan1

1Research Center for Environmental Change, Academia Sinica, Taipei, Taiwan
2Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan

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I. Description of TaiESM

Taiwan Earth System Model (TaiESM) is the climate model developed in the Consortium for Climate Change Study (CCliCS) supported by the Ministry of Science and Technology, Taiwan. Based on the framework of Community Earth System Model V.1.2.2 (CAM5.3: Neale et al. 2012, CLM4.5: Oleson et al. 2013, POP2: Smith et al. 2010), TaiESM includes many innovative representations of physical processes developed in-house in CCliCS.
Innovative developments of the first version of TaiESM include : 1) radiation effects of 3-dimension topography (Lee et al. 2013); 2) Statistical-Numerical Aerosol Parameterization aerosol scheme (SNAP; Chen et al. 2013); 3) GFS-TaiESM-Sundqvist macrophysics scheme (GTS; Wang et al. 2016, Shiu et al. 2016); 4) improved convective triggering of the deep cumulus scheme (Wang et al. 2015; Zhang and McFrarlane 1995), 5) an optional high vertical resolution ocean mixed layer model (Snow-Ice-Thermocline Model: SIT; Tsuang et al., in preparation), and a land surface irrigation scheme (Lo and Famiglietti 2013). More details of these schemes can be found in the literatures. Currently, the TaiESM v.1 is under evaluation as a fully coupled earth system model to prepare for long-term simulations for projecting and understanding the responses of climate systems to anthropogenic forcings.
A research version of TaiESM is also maintained as a platform for future model development. The physical schemes under development in the research version are 1) a two-moment bulk cloud microphysics scheme for stratiform clouds (CLR; Chen and Liu, 2004; Cheng et al., 2007, 2010), 2) a parameterization for gravity wave drag generated by deep, frontal, and shallow convection (Beres et al. 2004, Beres 2004), and 3) groundwater specific yield parameterization (Lo et al., in preparation).


Figure 1 : Illustration of physical schemes in TaiESM in the earth system.
Red color shows the in-house components of TaiESM v.1 developed in CCliCS.



Atmospheric ModelCAMv5.3TaiESMv1
Deep & Shallow Convection, Planetary Boundary layer Zhang-McFarlane/Park and Bretherton

NCEP/GFS Physics (Han and Pan 2011)
ZM + trigger function of SAS (Wang et al., in revision)

Cloud Fraction Park et al. (2014) Wang et al. + Shiu et al. (in preparation)
Radiative Transfer RRTMG (Iacono et al. 2008) CLIRAD (Chou et al. 2001)
Topography Effect on Solar Radiation (None) Lee et al. (2011, 2013)
Gravity Wave Drag Topographic Topographic, Frontal, Deep & Shallow Convections (Beres et al. 2004, Beres 2004)
Aerosol MAM3 (Liu et al., 2012) SNAP (Chen et al., 2013)
Warm Cloud Microphysics in Convection (None) Chen and Liu (2004)
Stratiform Cloud Microphysics MG1 (Morrison and Gettelman, 2008) CLR (Chen and Liu, 2004; Cheng et al., 2007, 2010)
Land Model CLM TaiESM
Irrigation (None) Wey et al. 2015
Groundwater Specific Yield Globally Constant Region Dependent
Ocean Model POP TaiESM
High Vertical Resolution Mixed Layer Model (None) SIT (Tsuang et al.)
Wave-induced Vertical Mixing (None) Qiao et al. (2004)
Ocean GCM POP2 TIMCOM (Tseng et al.)

 Table 1 : List of physical schemes used in TaiESM and its base model CESM. Red boxes show the components that are included in TaiESM v.1.



II. Important Components of TaiESM

II-1. Radiation Effects of 3D Topography

Figure 2 : Illustration of the parameterization for 3-D topography-radiation interaction.
It accounts for the impact of mountains on surface solar radiation such as shadow and reflection (Lee et al. 2013). 
Difference of surface solar flux at surface due to the Tibetan Plateau is shown on the right.



II-2 SNAP aerosol scheme




 Figure 3 : Schematic of aerosol scheme SNAP. Rate processes being parameterized were included in solid boxes,
while diagnostic formulas provided in dashed box.



II-3 GTS Macrophysics scheme

CAM5 macrophysics

GTS macrophysics



Figure 4 : Schematic of critical relative humidity used in CAM5 and GTS schemes. The GTS scheme
(Wang et al. 2016, Shiu et al. 2016) utilizes an PDF-consistent method to replace ad-hoc humidity
threshold used in CAM5 (Park et al. 2014).



II-4 Convective triggering of the deep convection scheme

Improved convective triggering of deep convection (Wang et al. 2015) is found to improve the simulated diurnal rainfall variation over the coastal and land regions, such as Borneo island and the Southern Great Plains.



PBL constraint on launching parcels

CIN constraint

Launching parcels within PBL


 TaiESM:ZMMOD  Launching parcels below 600hPa

Suppression when CIN too large

  Table 2 : List of two improved convective triggering of the deep convection scheme in TaiESM.



II-5 Optional high-resolution ocean mixed layer model : SIT




Figure 5 : Schematic of Sea-Ice-Thermocline model (SIT) and locations of upmost 11 vertical layers.






Figure 6 : Zonal wavenumber-frequency spectra for equatorial 850 hPa zonal wind show significantly improved simulation of Madden-Julian oscillation when CAM5 is coupled to SIT.


III. Current tests

III-1. AMIP runs : driven by climatological sea surface temperature







Figure 7:

Biases of annual temperature of TaiESM (upper left) and CESM1.2.2 (upper right) compared with ERA-Interim reanalysis (lower left).













Figure 8 :

Biases of annual relative humidity of TaiESM (upper left) and CESM1.2.2 (upper right) compared with ERA-Interim reanalysis (lower left).



F19 (2x2)


CAM5.3 (mean/RMSE)

TaiESM v.1 (mean/RMSE) 


 0.81 2.11 12.39 3.129 11.448


239.669 234.967 8.781 236.428 7.879


240.478 239.149 13.967 241.627 12.346
CLDTOT_CLDSAT 66.824 64.111 9.871 71.071 11.699


26.063 24.097 6.779 23.58 6.234
SWCF_CERES-EBAF -47.146 -52.156 15.981 -51.877 14.065


2.674 2.967 1.091 2.975 1.065


24.247 25.635 2.564 25.252 2.477


40.33 38.173 9.37 45.4 10.9


43.01 43.629 12.78 43.42 18.29


32.16 27.223 8.03 30.83 7.34

Table 3 : Global annual mean of observations, and CAM5 and TaiESM driven by SST and sea ice climatology. Root-mean-square errors between model and observations are also listed.





Figure 9 : Annual implied northward transports of ocean heat derived from NCEP reanalysis (black), CAM5.3 (red, left panel), and TaiESM (red,right panel) in AMIP-type setup.



III-2. Current tests of TaiESM as an earth system model

TaiESM currently is being tested in fully-coupled setup as an earth system model for parameter optimization to reach energy balance in the pre-industrial scenario. Preliminary results show at least 50-100 years are needed for model equilibrium.



IV. Research Version of TaiESM

IV.1 CLR Microphysics



Schematic of microphysical processes (arrows) for mass and number concentration of CCN (red boxes) and hydrometeors (blue boxes) included in the CLR scheme.



The cloud microphysical scheme used is based on CLR (Chen and Liu, 2004; Cheng et al., 2007, 2010). This scheme is capable of simulating aerosol-cloud interactions through detailed coupling between cloud and aerosol microphysical processes.


IV.2 Regional dependence of ground water yield based on GRACE satellite

 The groundwater specific yield is derived regionally from satellite GRACE and implemented into CLM.



IV.3 Adding gravity wave drag and increasing vertical resolution for QBO

To improve QBO simulation, a parameterization of gravity wave drag is added for those induced by orography, frontal genesis, and deep and shallow convection. It is found that QBO cannot be generated at a coarser vertical resolution.




IV. Reference

Beres, J. H., M. J. Alexander, and J. R. Holton (2004), A method of specifying the gravity wave spectrum above convection based on latent heating properties and background wind, J. Atmos. Sci., 61, 324– 337.
Beres, J. H. (2004), Gravity wave generation by a three-dimensional thermal forcing, J. Atmos. Sci., 61, 1805–1815.
Chen, J.-P., I.-C. Tsai, and Y.-C. Lin (2013), A Statistical-Numerical Aerosol Parametierzation Scheme, Atmos. Chem. Phys., 13, 10483-10504.
Chen, J.-P., and S.-T. Liu (2004), Physically based two-moment bulk water parameterization for warm-cloud microphysics, Q. J. R. Meteorol. Soc., 130, 51–78.
Cheng, C.-T., W.-C. Wang, and J.-P. Chen (2007), A modeling study of aerosol impacts on cloud radiative properties and precipitation. Q. J. R. Meteorol. Soc., 133, Part B, 283–297.
Cheng, C.-T., W.-C. Wang, and J.-P. Chen (2010), Simulation of the effects of increasing cloud condensation nuclei on mixed-phase clouds and precipitation of a front system. Atmos. Res., 96, 461–476.
Lee, W.-L., K. N. Liou, and C.-c. Wang (2013). Impact of 3-D topography on surface radiation budget over the Tibetan Plateau. Theor. Appl. Climatol., 113, 95-103.
Wey, H.-W., M.-H. Lo, S.-Y. Lee, J.-Y. Yu, and H.-H. Hsu (2015), Potential impacts of wintertime soil moisture anomalies from agricultural irrigation at low latitudes on regional and global climates, Geophys. Res. Lett., 42, 8605–8614, doi:10.1002/2015GL065883.
Neale, R. B., and coauthors, 2012, Description of the NCAR Community Atmosphere Model (CAM5.0). NCAR Tech. Note NCAR-TN-486+STR, 274pp.


V. Acknowledgment

The Community Earth System Model (CESM) is developed and maintained by the Climate and Global Dynamics Division at National Center for Atmospheric Research (NCAR). This research was supported by the Ministry of Science and Technology, Taiwan, R.O.C., under Grant No. NSC100-2119-M-001-029-MY5.