Design and evaluation of theNational Institute for Environmental Studies

(NIES) transport model

Dmitry Belikov and Shamil Maksyutov

National Institute for Environmental Studies, Tsukuba, Japan

The 2010 Workshop on the Solution of Partial Differential Equations on the Sphere

Model formulation

New version of the NIES TM (NIES-08) with flux-form advection algorithms have been designed. Just like in the predecessor model with semi-Lagrangian algorithms (Maksyutov et al., 2008), we presented the atmospheric constituent transport equation in the Lagrangian-style form (Willamson and Laprise, 2000):

cos( )

k k kk k kdq q qq F S

dt t

R R

V

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A reduced latitude-longitude grid scheme

The offline global tracer transport model version uses a reduced latitude-longitude grid scheme (Peterson et al., JGR, 1998), in which the sizes of grids are doubled several times approaching the poles

Advantages vs. icosahedral, cubic grids – easy to bring reanalysis data, and formulate 2nd, 3rd order approximations

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Horizontal mass flux correction method

= , 1,..,sh c

ml F l l N

t

������������������������������������������

= ; 1,..,sc h

mF l l N

t

��������������������������������������������������������

The horizontal mass fluxes, derived from the spectral data (the output of weather forecast models) are balanced with the surface pressure tendency by adding correction fluxes, which is necessary to be determined (Heimann and Keeling, Geophys. Mon., 1989)

The correction flux is calculated by transforming Equation

into a Poisson equation, which is solved with a discrete 2D Fourier transform for every level l

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NIES TM transport algorithm test

Initial tracer field

velocities

SML – Semi-Lagrangian (Maksyutov et al. 2008);

VL – 3-rd order van Leer scheme (van Leer, 1977); Pr – Second Moments (Prather, 1986)

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NIES TM transport algorithm testResolution NIES-08\SML NIES-08\VL NIES-08\Pr

2.5º 2.5º

CPU, sec 6.08 10.21 292.90emin –5.37E-03 6.22E-04 1.38E-04emax 2.16E-03 –2.86E-03 –3.48E-04err1 6.09E-02 –6.66E-03 –5.96E-08err2 5.08E-03 1.17E-03 1.02E-03Memory, GB 0.72 0.72 0.77

0.625º 0.625º

CPU, sec 82.20 370.975 12683.15emin –1.04E-07 3.93E-05 –5.11E-06emax 7.95E-08 –2.44E-03 –5.21E-03err1 1.59E-02 –1.75E-03 5.55E-03err2 1.74E-02 6.15E-04 1.18E-04Memory, GB 1.94 1.94 2.46

SML – Semi-Lagrangian (Maksyutov et al. 2008),VL – 3-rd order van Leer scheme (van Leer, 1977), Pr – Second Moments (Prather, 1986)

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NIES TM meteorology data

• The Japan Meteorological Agency (JMA) Japan Climate Data Assimilation System (JCDAS) meteorological dataset (6-hourly time step, resolution of 1.25×1.25 deg, 40 hybrid vertical levels). Height of planetary boundary layer with time step of 3 hours are taken from ECMWF Interim Reanalysis.

• Global Point Value (GPV) - a special product prepared by the Japan Meteorological Agency Global Spectral Model (JMA-GSM) (3 hourly time step, resolution of 0.5 0.5 deg, 21 pressure levels).

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High resolution: 0.625 deg globally

Simulated surface CO2 concentration around Japan at 21:00UTC, March 26, 2008 using NIES-08 with resolution 0.625 deg

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Hybrid sigma-pressure and sigma-isoentropic vertical coordinate systems

A hybrid sigma-pressure and a sigma-isentropic vertical coordinate systems with 32 levels up to 2 mb

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Hybrid sigma-pressure and sigma-isoentropic vertical coordinate systems

Pres

sure

, hPa

Mean age of air (SF6) simulated by the NIES-08 with sigma-pressure (left) and sigma-isoentropic (right) vertical coordinate systems

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NIES TM results (SF6)

Interhemispheric gradients of modeled and observed SF6 concentrations

Zonally averaged annual mean of SF6 concentration simulated by NIES-08

-90 -60 -30 0 30 60 900.0

0.2

0.4

0.6

0.8NIES-08/SML/2.5NIES-08/VL/2.5NIES-08/Pr/2.5WDCGG

Lat, deg

SF6,

ppt

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NIES TM results (CO2)

Latitudinal distributions of CO2

seasonal amplitude at 35 GLOBALVIEW-CO2 (2008) sites. Seasonal amplitude is the difference between the maximum and the minimum of seasonal cycle.

-90 -60 -30 0 30 60 900

3

6

9

12

15

18N-08/VL/2.5N-08/SML/2.5N-08/Pr/2.5Obs.

Lat, deg

CO2,

ppm

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Convective parameterization scheme

• Kuo-type cumulus parameterization (Grell, 1994) including entrainment and detrainment processes on convective updrafts and downdrafts proposed by Tiedtke (1989);

• new method to determine cumulus convective updrafts

,base

convu q

PM

where Pconv denotes the convective precipitation rate at the surface [kg/m2/sec], qbase is the absolute humidity at the cloud base [kg/kg];

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Convective parameterization scheme

Seasonally average convective mass flux (g/m2/sec) from the NIES TM and Modern Era Retrospective-analysis For Research And Applications (MERRA) data for summer 2006

NIES TM MERRA

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NIES TM results (222Rn)

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Without convective parameterization

With new convective parameterization

15

NIES TM results (222Rn)

The model results are compared with data from in situ observations (Kritz et al., JGR, 1998; Liu et al., JGR, 1984; Zaucker et al., JGR , 1996) and the results obtained from model GAMIL (Zhang et al., ACP, 2008)

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ConclusionImprovements in tracer transport simulation are achieved due to:

• Mass conservative numerical algorithm and horizontal mass flux correction method;

• A reduced latitude-longitude grid scheme;• Hybrid sigma-pressure and a sigma-isentropic

vertical coordinate systems;• Convective parameterization scheme;

Future:

• High-resolution meteorological data;

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Thank you!

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