AICS TECHNICAL REPORT
N0. 2017-001

Modification of a baroclinic test case to aheight-based coordinate system without
topography implementation
By

Ryuji Yoshida1,2, Hisashi Yashiro1, Masaki Satoh3,
and Hirofumi Tomita1,4
1: RIKEN Advanced Institute for Computational Science,
7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650–0047, Japan
2: Research Center for Urban Safety and Security, Kobe University, Kobe, Japan,
3: The University of Tokyo, Atmosphere and Ocean Research Institute, Kashiwa, Japan,
4: Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.

Submitted on 07/07/2017
Accepted on 04/08/2017
Published and copyrighted by
RIKEN Advanced Institute for Computational Science (AICS)
7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, 650-0047, Japan

Modification of a baroclinic test case to a height-based
coordinate system without topography implementation
Ryuji Yoshida1,2, Hisashi Yashiro1, Masaki Satoh3,
and Hirofumi Tomita1,4
1: RIKEN, Advanced Institute for Computational Science, Kobe, Japan,
2: Research Center for Urban Safety and Security, Kobe University, Kobe, Japan,
3: The University of Tokyo, Atmosphere and Ocean Research Institute, Kashiwa, Japan,
4: Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.

Abstract
In this study, a baroclinic wave test case is modified to be conducted without topography
implementation in a model with a height-based vertical coordinate system (VCS). In the original
test case, the topography implementation is required for a model with a height-based VCS, unlike
a model with a pressure-based VCS. We propose a method to conduct the test case without the
topography implementation using a model with a height-based VCS. Compared with the original
test case, the proposed method provides similar wave structures and evolutions. The values andtime evolutions of root mean square errors are almost the same as the original test case. The
convergence of the numerical solution can be examined by the proposed method at a degree similar
to that of the original method. This means that the proposed method examines the physicalperformance of the model without the topography implementation with a height-based VCS.

Keywords: Global atmospheric model; Dynamical core; Ideal test case; Baroclinic wave test

Corresponding author: Ryuji Yoshida, RIKEN Advanced
Institute for Computational Science, 7-1-26 Minatojimaminami-machi, Chuo-ku, Kobe, Hyogo 650-0047, JAPAN.
E-mail: ryoshida@riken.jp
Tel: +81-78-940-5735
Fax: +81-78-304-4963

1

1Introduction
Ideal test cases performed on the dynamical core of an atmospheric model are necessary toevaluate its physical performance and detect coding bugs. A test suite on the shallow waterequation (Williamson et al. 1992) has been typically used as the standard test case to examine thephysical performance of the two-dimensional dynamical core. As for a three-dimensional test case,
Held and Suarez (1994) proposed a popular test case to investigate the physical performance
climatologically. A deterministic test can directly detect the model error and/or the convergence ofthe numerical solution by calculating L2 norm, which represents the root mean square using theexperimental results with a high-resolution result as the reference. Among the three-dimensional
test cases, the baroclinic wave test cases are particularly useful as deterministic test cases, because
the baroclinic wave is one of the most important phenomena for numerical weather prediction.
Polvani et al. (2004) proposed a deterministic baroclinic wave test case for the three-dimensional
global atmospheric model and realistic baroclinic waves were simulated in the test case. This typeof test case is very useful because different amplitudes and phases of multiple waves can be
examined by starting from a single perturbation rather than a periodic perturbation. However, thetest case is not suitable to investigate the reproducibility of fine structures that appear in the highresolution run, because this setting prescribes an explicit horizontal diffusion. Jablonowski andWilliamson (2006a) developed a new baroclinic wave test case (hereafter JW06) that does not need
the use of such a diffusion in their experimental setting. This provides a significant advantage tothe evaluation of the convergence of the numerical solution. For this reason, JW06 has been
recently used in model development as the standard test case for baroclinic waves (Skamarock et
al. 2012, Ullrich and Jablonowski 2012, Park et al. 2014, Zangl et al. 2015).
The JW06 test case is applicable to a wide variety of models. However, the implementation of
topography is also required when we conduct the JW06 test case on a height-based verticalcoordinate system (VCS). The initial condition of the baroclinic state is defined by the hydrostaticbalance on the pressure-based VCS. The geopotential field at the geoid surface with a meridional
gradient must be given as the lower boundary condition. For height-based VCSs, the method of
conversion of the initial pressure-based VCS to the height-based VCS is explained in JW06 tosatisfy the lower boundary condition. As a result, the lower boundary condition of the geopotential
field must be treated as “topography.”
The topography implementation is one of the important steps of model design. It strongly affects
the physical performance of the model. The physical performance of pure dynamical core should2be examined before applying the topography. Although the JW06 test case has been conducted inmany models and will be used for many new models in the future, it has an above lack for thedefault use for height-based VCSs. Therefore, the JW06 test case should be applied to the heightbased VCSs without topography implementation. Ullrich et al. (2014) has resolved this issue by a
new analytical solution of initial condition. However, JW06 is still useful to compare results of a
newly developed model with those of many models that have already conducted, because many
knowledges in JW06 have been accumulated during long time so far.
In this study, we propose a modified method of the JW06 test case that enables to the dynamicalcore to function without topography implementation on the height-based VCS. The Nonhydrostatic ICosahedral Atmospheric Model (NICAM; Tomita and Satoh 2004; Satoh et al. 2008;2014) was used to show the effectiveness of the modified method. We described the details of themodified JW06 test case method in Section 2 and discussed the experimental results of themodified method in comparison with the original test case in Section 3. Finally, we summarized
the results in Section 4.

2

A modified method for the JW06 baroclinic wave test caseTo describe the initial conditions, we assume five prognostic variables: three-dimensional windspeed components (u, v, and w; zonal, meridional and vertical velocities, respectively), potential
temperature (T), and geopotential ( ). In addition, a spherical coordinate with the pressure-based, ,VCS is assumed. Temperature is indicated bylatitude, and, whereis the longitude,

is theis the vertical coordinate defined by a pressure-based hybrid coordinate system(Jablonowski and Williamson 2006b, defined asAB, where A and B are thespecified coefficients. The coefficients are such that A = 0 and B = 1 at the bottom boundary (seeJablonowski and Williamson (2006b) for a detailed description of the setting of coefficients).
denote the reference pressure (105 Pa) and surface pressure (and

10 Pa ,

respectively.
The initial conditions for the wind and potential temperature fields are defined based on JW06.
In the JW06 test case, the auxiliary variable

is defined by(1)

0.252. Using Eq. (1), the zonal wind speed is defined as

with
, ,
where u

cos

⁄

sin

2

(2)

35 ms-1 is the base wind speed. The structure of the zonal wind is zonally3homogenous. At the initial conditions, meridional and vertical wind speed components are set to
zero,
, ,

0

(3)

, ,

0

(4)

The temperature field satisfying the thermal-wind balance with a given zonal wind is given by
〈

, ,
2

〉

⁄

cos

cos

6.371229

where

sin

cos

⁄

2 sin

sin

cos

Ω

(5)

10 m is the mean radius of the Earth and Ω

the angular velocity of the Earth. In Eq. (5), 〈

7.29212s-1 is10〉 means horizontally averaged temperature andis defined by two different equations for different heights,
〈

〉

/

〈

〉

/

where

,

for

(6)

∆

, for

(7)

= 288 K is the horizontally averaged temperature at the surface (ps). Height level is
1 andspecified by a dimensionless number and the surface level and tropopause level are
0.2 , respectively. ∆

4.8

10

is the empirical temperature difference. The dry

287.0 J kg-1K-1 and the gravity acceleration is

ideal gas constant is

9.80616 ms-1.

The geopotential field of the initial condition is defined by
〈

, ,
cos

〉

⁄

⁄

cos
cos

2 sin

sin

cos
Ω

(8)

where
〈

〉

〈

〉

/

1
Γ

,

for

(9)

1

∆

5

5

, for

Geopotential is used to solve the relationship between(10)-levels and height-levels with an iterative

method based on Newton’s method. The height-levels (z) are the prescribed values by a user. Basedon JW06, the method is given by
, ,
⁄

(11)

, ,

where n denotes the iteration count and F is the forcing function given by
, ,

, ,

(12)
4

, ,

, ,
10

The initial value of(13)
is recommended in JW06 for the iterative method. The firstestimated values are calculated using Eqs. (1), (2), and (5)-(10) with the value of
estimated

is updated using Eqs. (11)-(13). The absolute error |and the| decreases as

iteration proceeds. When the absolute error decreases to a small enough value, the iteration is
terminated. The final estimated values of the prescribed height-levels are used as thecomputationally analyzed initial state.
The definitions of the initial condition by Eqs. (2)-(10) are the same as the original JW06. The
bottom boundary condition of the geopotential distribution at

is treated as the topography

distribution for the height-based VCS in the original JW06 test case. This method requires anadditional implementation of the topography scheme. In this study, we propose a modified methodby converting from a geopotential distribution to a surface pressure distribution instead of the extra
implementation of topography scheme. Referring to Jablonowski et al. (2008), the equation for the
topography height (H) is given by substituting

1 in Eq. (8) and dividing it bygravitational acceleration g,
m

cos

1

sin

cos

1

.

2sin

cos

(14)

Using this topography height, we estimate the surface pressure. Pressure P2 can be calculated using
the reference pressure P1 at a different height at the same horizontal location in the equation ofhydrostatic balance,(15)
We estimate the surface pressure by extending Eq. (15) to the ground level and using the reference
values for P1 and z1. We assume that the horizontal distribution of density is smooth and the density
value is almost the same as the value at the neighboring location in the layer between z1 and z2.
By this assumption, the surface pressure at the target location can be estimated by the height
difference between the target location and the other location horizontally removed from the target.
Thus, in Eq. (15), density ( ) and height (z) are replaced by surface density ( ) and topographyheight (H), respectively. Then, we obtain a relationship between the surface pressures ofhorizontally neighboring locations,(16)
5

where the suffixes (1, 2) for S and H indicate different latitudinal locations.

can be obtainedby the state equation of an ideal gas using the predetermined surface temperature and topographyheight from Eqs. (5) and (14). To calculate
surface pressure atlatitudeis,should be a known reference value. The

10 Pa and topography (i.e., geopotential height at) is 0 m at

, derived from Eq. (14) by assigning 0 m to H and solving for

0.691590985442682 rad . We call(, at this latitude, the reference surface pressure

10 Pa), and H, at this latitude, the reference topography height
can then calculate the surface pressure everywhere using

and

0 m). We.

To apply the modified method to the atmospheric models, we discretized the integration in Eq.
(16) through the trapezoidal rule,g(17)

where j is the meridional grid point index. We used the same reference point () for alllatitudinal locations so as to easily calculate the grid points according to the tested model. Although
can be calculated by replacingin the neighboring grid point of j iteratively, the result

is not different from the case using a constant. Furthermore, using the relationship,

Eq. (17) is transformed as
/

where

,

is the surface temperature, derived from Eq. (5) by assigning

to

. The

distribution of surface pressure is determined by repeating this calculation for all grid points in themeridional direction. The vertical pressure profile is calculated using the equation of hydrostaticbalance (Eq. [15]) by vertical integration with the surface pressure as a lower boundary condition
for the prescribed height-levels. Up to this point, the steady state of initial conditions is given forthe height-based VCS without topography. The results of the steady state test case are explainedin Section 3.
For the wave developing experiment, an initial perturbation is required. Following JW06, theinitial perturbation is defined by
, ,
where

exp

,

⁄10 denotes the horizontal perturbation scale. r is defined byarccos sin

where

⁄

⁄9 ,

sin

cos

cos

cos

,

2 ⁄9 is the location of the initial perturbation and the perturbation is
6located at 20°E/40°N. In addition, the friction at the lower boundary should be neglected, i.e., slip
condition.

3

Test experiments of the modified method
3.1 Experimental settings
Recently, various global models with height coordinates have been developed, such as the

Model for Prediction Across Scales (MPAS, Skamarock et al. 2012) and the ICOsahedral Nonhydrostatic (ICON) model (Zangl et al. 2015). In this study, we used NICAM (Tomita and Satoh2004; Satoh et al. 2008; 2014) to examine the modified method of the JW06 test case. The
horizontal discretization of NICAM is an icosahedral grid system with an Arakawa A-grid on thesphere and the vertical grid coordinate is a height-based VCS with a Lorenz type staggered grid.
The topography representation is implemented by the terrain following coordinate. The temporal
discretization is the third order Runge-Kutta scheme and an explicit numerical filter based on the
fourth order hyper-diffusion is used. Although the native grid topology in NICAM is an icosahedral
grid system, each of the grid points has the latitude-longitude information. Therefore, the initialstate can be directly calculated by the method described in Section 2.
We used two different horizontal resolutions and three different vertical grid configurations to
examine resolution dependency. The horizontal grid spacings were 240 km (glevel-5) and 120 km(glevel-6), where “glevel-n” represents the horizontal grid resolution in the icosahedral grid,
generated by the nth recursive division (Tomita et al. 2001). The vertical grid arrangements had 40,

Fig. 1 The initial condition in glevel-6 (120 km) with 160 levels. (a) The initial condition
calculated by the modified method. Black contours represent temperature, blue contours
represent pressure, and red contours represent zonal wind speed. The structure is homogenous in
the longitudinal direction. (b) and (c) are the difference of zonal wind speed () andthe difference of temperature (), respectively. ORG means the original method andMOD means the modified method.
7

Table 1 Experimental model settings

80, and 160 levels with a constant grid spacing of 1,125.0, 562.6, and 281.3 m, respectively. We
conducted two types of experiments: a steady state experiment (without an initial perturbation)
and a wave developing experiment. The time integration period was 30 d for the steady stateexperiment and 12 d for the wave developing experiment. Other experimental settings are shownin Table 1. The calculated initial condition in glevel-6 with 160 levels is shown in Fig. 1. The zonal
wind jet, temperature, and pressure fields were symmetrically distributed with respect to theequator (Fig. 1a). The difference between the zonal wind speeds (udifference between the temperatures ( t) and the) are shown in Fig. 1(b) and (c),respectively. The maximum wind speed difference was approximately |1

10 | ms-1 that is0.003% of the base wind speed (35 ms-1). The temperature difference was also small,approximately |5

10 | K that is 0.002% of the horizontally averaged surface temperature(288 K). Therefore, the initial condition of the modified method was almost the same as the original
method.

3.2 Experimental results
To examine the impact of changing the initial condition using the modified method, we
compared the results of the steady state experiment using the modified method (MOD) with thoseobtained using the original method (ORG). For the analysis, we used a latitude-longitude grid data
converted from the original icosahedral grid system using linear interpolation. To evaluate theeffect of maintaining the initial state, we calculated the root mean square error L2 norm byreferring to JW06.
L2 norm

850

850

where the over bar
sin

⁄

sin

∑

,

,
∑

⁄

,

denotes the zonal average, t0 denotes the initial time,⁄is the weighting function where the half indices denote the locations of8

Fig. 2 L2 norm (root mean square error) of zonal wind speed at 850 hPa (U850) for the steady stateexperiment for (a) glevel-5 (g5; 240 km) and (b) glevel-6 (g6; 120 km). ORG means the original
method and MOD means the modified method.

Fig. 3 Temperature and surface pressure distributions for the modified method in glevel-5 (240km) with 160 vertical levels. Only the northern hemisphere is shown in each panel.
cell interfaces in the meridional direction. The temporal evolutions of L2 norm are shown in Fig.
2 for both glevel-5 and glevel-6. For all experimental settings, the initial state is maintained withan error less than 1 ms-1 for the first ten days. An error of 1 ms-1 is about 3% of the base windspeed (35 ms-1). The period in which the initial state is maintained with less than 1 ms-1 of error iscalled “the quasi steady state (QSS) period” hereafter. The error rapidly increased around day 13
for glevel-5 and around day 15–16 for glevel-6. Because the accuracy of the horizontal partial
difference in glevel-6 was higher than that in glevel-5, the QSS period in glevel-6 was longer thanthat in glevel-5. The length of the QSS period in MOD was similar to that in ORG for eachhorizontal grid space. Therefore, the initial condition of MOD satisfies a balance similar to ORG.
The error was reduced at higher vertical resolutions during the QSS period. In glevel-6 of MOD,9

Fig. 4 Temperature distribution at 850 hPa on day 9 in glevel-5 (240 km). Only the northernhemisphere is shown in each panel. ORG means the original method and MOD means themodified method.

Fig. 5 Same as Fig. 4 but for glevel-6 (120 km).
10

Fig. 6 Temperature distributions at 850 hPa on day 9 for (a) and (b), and the difference of thetemperature distribution (t850
850 ) for (c) and (d). (a) and (c) are glevel-5 (240km) with 160 vertical levels, and (b) and (d) are glevel-6 (120 km) with 160 vertical levels. The
back contour is ORG and red contour is MOD. ORG means the original method and MOD
means the modified method.
the average error values for 40, 80, and 160 levels were 0.7, 0.3, and 0.2 ms-1, respectively. The
QSS period tended to be shorter for higher vertical resolutions regardless of horizontal grid spaces
and methods of initial state creation. Further investigation of these characteristics is needed infuture studies.
We next explain the results of the wave developing experiment. Figure 3 shows the developmentof the simulated waves in MOD with glevel-5 and 160 vertical levels. It is clear from the surfacepressure field that several mid-latitudinal lows have developed by day 9. Figures 4 and 5 show thetemperature distributions for glevel-5 and glevel-6, respectively. The structure of the temperature
distribution in MOD was quite similar to that in ORG (Figs. 4 and 5). To clarify thesecharacteristics, the temperature distribution on day 9 of MOD was compared directly to that of
ORG for glevel-5 and glevel-6 with 160 levels (Fig. 6). The horizontal structure of the temperature
for MOD was similar to that of ORG regardless of horizontal grid spaces. Although the locationsof the wave phases in MOD were slightly shifted westward compared with those in ORG (Fig. 4aand 4b), the structure of the simulated waves in MOD was qualitatively the same as that in ORG.
Thus, the modified method in the height-based coordinate model has the potential to simulate thebaroclinic waves as well as the original method in pressure-based coordinate models.
Comparison of different vertical resolutions (Fig. 4a with Fig. 4c, or Fig. 4d with Fig. 4f) showedthat the wave structure sharpened with an increasing number of vertical levels and this is a common
feature of ORG and MOD. Comparison of different horizontal grid spaces (Fig.4c with Fig. 5c or
11Fig. 7 Spectral density of zonal wind speed at 850 hPa for the wave developing experimentwith 160 vertical levels. ORG means the original method and MOD means the modified
method.
Fig. 4f with Fig. 5f) showed that the wave structure of glevel-6 was sharper than that of glevel-5,
and this is the same in the other vertical grid settings. The power spectral density of zonal windspeed at 850 hPa is shown in Fig. 7 for both glevel-5 and glevel-6 with 160 vertical levels. The
spectral density in glevel-5 was similar to that in glevel-6 around 3,000 km of wavelength (Fig. 7).
Since this wavelength corresponds to that of the baroclinic wave, the outlines of the waves were
simulated with a similar magnitude in both glevel-5 and glevel-6. The spectral density at
wavelengths shorter than 3,000 km in glevel-6 was much higher than that in glevel-5. This
indicates that the numerical solution is refined by increasing the horizontal resolution. These
characteristics in MOD are the same as in ORG in terms of the correspondence between theoriginal and modified test cases. Thus, the simulated phenomenon in MOD is the same as that in
ORG. Although topography implementation is necessary in ORG, it is not required in MOD.
To assess the error in the wave developing experiment, we calculated the L2 norm of the zonal
wind speed at 850 hPa. We assume that the results of the numerical simulation with high resolution
are close to the real solution. Thus, L2 norm is defined as,
L2 norm

∑ ∑

,

,
∑ ∑

,

,

⁄

(18)

where u850 and u850ref denote the data of the assessment target and of the reference solution,
12

Fig. 8 L2 norm (root mean square error) of zonal wind speed at 850 hPa (U850) for the wave
developing experiment using (a) the original method (ORG) and (b) the modified method(MOD).

Fig. 9 Slope of the convergence for numerical solution. Rate of error (R) is calculated as R
L2 norm⁄L2norm , where L2 norm is root mean square error for surface pressure (PS),temperature at 850 hPa (T850), and zonal wind speed at 850 hPa (U850) for each horizontal
grid spacing. L2 norm is root mean square error of g-level 5 (240 km) for each variable. Allof L2 norm are calculated on the 7th day after the initial time.
respectively. The simulated result by NICAM in 14-km horizontal grid spacing with 160 verticallevels was used as the reference solution. L2 norm for ORG and MOD are shown in Fig. 8 (a) and(b), where the reference solution is used with the same initial state method as each assessment
target. The error rapidly increased with time in settings with 40 and 80 vertical levels regardlessof the horizontal grid spacing because of an imbalance in the initial condition with respect to thenon-hydrostatic model (Skamarock et al. 2012). The error decreased after day 3 and the initial
13

shock became steady by day 5 in glevel-5 cases. Note that this not only occurred in MOD but alsoin ORG. The initial shock can be reduced by recalculating the initial state of the wind field basedon the thermal-wind balance (Skamarock et al. 2012). The initial error is much lower in the resultsof the cases with 160 vertical levels both for ORG and MOD. The wave patterns became significant
on day 5 and one of the waves reached maturity by day 9 (Fig. 3). Comparing Fig. 8(b) with (a),the error of MOD was quite close to that of ORG and the temporal variation of MOD was similar
to that of ORG. The error increased rapidly from day 4 to day 9 because the baroclinic waves
developed rapidly during this period. This period was the focus point of the baroclinic wave test
case. In this period, the error exceeded 1 ms-1 that was smaller than the error of the steady state
test case with 80 or 160 vertical levels. Therefore, MOD has the potential to evaluate the model
performance with the same degree of error as ORG and could be applied for comparing the model
performance with pressure-based VCS.
Examining the convergence of the numerical solution is important for the deterministic test case.
We examined the convergence using the L2 norm for both ORG and MOD on the 7th day after the
initial time. For an accurate assessment, we performed additional calculations with finer horizontal
grid spacings: 60 km (glevel-7), 30 km (glevel-8), and 14 km (g-level 9). The vertical gridconfigurations had 160 levels for each horizontal grid spacing and other settings were the same as
the experiments above. To evaluate the convergence of the numerical solution, the results from the
14-km grid spacing were used as the reference solution in L2 norm calculation by Eq. (18). The
convergence for four different horizontal grid spacings is shown in Fig. 9. The slope of NICAM
was between the first order and the second order slopes regardless of the variables. Since thehorizontal discretization of NICAM is a second-ordered scheme, the performance in Fig. 9 isreasonable. The measured performance of the convergence in MOD was almost the same as thatin ORG. Therefore, we can examine the convergence by using MOD without terrain
implementation on the model of the height-based VCS.

4

Summary
In this study, we propose a modified method of the baroclinic wave test case of JW06 for global

atmospheric models. Topography implementation is not required to conduct the test case on amodel with a height-based VCS. In the original method, the test case is not able to perform the
dynamical core without the topography implementation. In the modified method, the topography
profile is converted to surface pressure, keeping the consistency between the horizontal and14

vertical balances. We examined the modified method using NICAM. In the steady state experiment,
the period during which the initial state was maintained with an error of less than 1 ms-1 in u850
was similar in length for MOD and ORG. In the wave developing experiment, the error of themodified method in the wave developing period was quite similar to that of the original method.
The simulated baroclinic waves in the modified method were close to those in the original methodwith respect to wave structures and phases. The modified method can examine the convergence ofthe numerical solution at degree similar to that of the original method, i.e., by using the modified
method proposed in this study, the baroclinic wave test case of JW06 becomes a more useful test
case for examining the model performance in the early stages of global atmospheric model
development with the height-based VCS.

Acknowledgements
We are grateful to Dr. Hiroaki Miura for valuable discussions. We express our thanks to Drs.
Masami Sakamoto and Hiromasa Yoshimura for their suggestions. We also thank the members of
the Computational Climate Science Research Team, RIKEN AICS, for their feedback. This work
was partly supported by JSPS G8-call ICOMEX.

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