The ECHAM-SIT-TIMCOM Model and potential applications · The ECHAM-SIT-TIMCOM Model (EHTW ESM) and...
Transcript of The ECHAM-SIT-TIMCOM Model and potential applications · The ECHAM-SIT-TIMCOM Model (EHTW ESM) and...
The ECHAM-SIT-TIMCOM Model (EHTW ESM) and
potential applications
Yangtze
River
Taiwan West Pacific Global Forecast System Planning Workshop
臺灣與西北太平洋氣候預測全球模式發展規劃研討會中央氣象局310會議室
CWB, Taipei, Taiwan, May 8-9, 2013
Ben-Jei Tsuang (莊秉潔), Department of Environmental Engineering, NCHU, Taiwan
Yu-Heng Tseng (曾于恒), NCAR, USA, NTU, Taiwan
Question? Can a coupled OAGCM model do a
better weather and climate forecast than an AGCM
driven by a good SST?
AGCM: Two-Tier Approach
1. SST prediction by statistical/physical model
2. SST used as the boundary condition to drive AGCM
OAGCM: One-Tier Approach
1. Prescribed ICs for both atmosphere and ocean
2. Run the coupled model with/without flux correction
(nudging)
Answer? (No/Yes) No, if the bias of the mean SST is too large by OAGCM, the
resolved shorter-time scale flux is not good enough.
1. ENSO can be well predicted by the two-tier approach.
2. Single coupled OAGCM usually can not beat multiple methods for
SST prediction in ENSO time-scale.
Yes, only the bias from the SST mean state by the coupled model
can be overcomed by a better resolving the air-sea interaction with
time scale short than the mean state.
1. For example, if the rainfall by diurnal cycle and Mudian-Jullian
Oscillation (MJO) can be better resolved by the coupled model.
2. 𝐿𝐸 = 𝑤 ∙ 𝑞 = 𝑤 ∙ 𝑞 + 𝑤′ ∙ 𝑞′𝑚𝑜
+ 𝑤′ ∙ 𝑞′𝑚𝑗𝑜
+ 𝑤′ ∙ 𝑞′ℎ
Test the idea!
AGCM run (prescribed SST, AMIP-type run) (ECHAM)
OAGCM run (ECHAM/SIT/TIMCOM)
AGCM + 1 d ocean model with nudging WT (ECHAM/SIT)
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AGCM: ECHAM5 The dynamical part of ECHAM is formulated in spherical harmonics. After the inter-
model comparisons by Jarraud et al. (1981) and Girard and Jarraud (1982) truncated expansions in terms of spherical harmonics were adopted for the representation of dynamical fields.
The transform technique developed by Eliasen et al. (1970), Orszag (1970), and Machenhauer and Rasmussen (1972) is used such that non-linear terms, including parameterizations, are evaluated at a set of almost regularly distributed grid points - the Gaussian grid.
In the vertical, a flexible coordinate is used, enabling the model to use either the usual terrain following sigma coordinate (Phillips, 1957), or a hybrid coordinate for which upper-level model surfaces atten over steep terrain, becoming surfaces of constant pressure in the stratosphere (Simmons and Burridge (1981) and Simmons and Struring (1981)).
Moist processes are treated in a different way using a mass conserving algorithm for the transport (Lin and Rood, 1996) of the different water species and potential chemical tracers. The transport is determined on the Gaussian grid.
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OGCM: TIMCOM
Mixed Arakawa A and C grids; Z-level, rigid-lid/free surface, fourth-order accurate;
Unfiltered etopo2 or etopo5 bathymetry;
< 10,000 lines
History of TIMCOM (3-
D Ocean model)POCM
(1988)
Bryan (1969)
Cox (1970)
Semtner
(1974)
Cox
(1984)
FRAM
(1991)
MOM1
(1990)
MOM2
(1995)
MOM2
(1996)
OCCAM
(1995)
Killworth
et al.(1991)POP
(1992)
POP
(1994)
POP
(1996)
CME
(1989)
CSM
(1996)
NCOM
(1993)
SOMS
(1987)
DIECAST
(1994)
DIECAST
(1997)
TIMCOM
(2010)
CANDIE
(1998)POP2
(2003)
MOM3
(1998)
MOM4
(2003)
LANL UK GFDL NCAR
PGCM
(1991)
TAIWAN
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SIT (Snow/Ice/Thermocline) Coupler
AtmosphereECHAM (AGCM)19/31 levels(T31-T213)
Ocean:TIMCOM (OGCM)31 Levels (T31-T213)
VD
IFF/SIT (A
ir/Sno
w/Ice/Th
ermo
cline
)2
sno
w+2
ice+4
1 w
ater levels
11 levels in the upper 10
m depth of ocean (0.5
mm, 1, 2, 3, .., 10 m)
Figure 2. ECHAM/SIT/TIMCOM model structure. Note that ocean grid
collocates with the atmospheric grid (or its subdivision equally).
SIT Coupler: based on Governing EquationsContinuity eqn.
Momentum eqn.
Conservation eqn. for temperature and salinity Eqn. of State
Hydrostatic Eqn.
Jan, 1957
Typhoon Morakot(8 August, 2009)
Rainfall, WT and EvapECHAM+3D Ocean (npo0nnnn run) (IC + nudging)
Preliminary Results of present (1945-
2010) climate simulation
AGCM: AMIP run driven by Hadley SST and seaice
(ECHAM)
AGCM+1-D ocean run: prescribed CO2, WT> 10 m depth
nudging with Ishii ocean profile. No nudging within 0-10 m
depth (ECHAM/SIT)
OAGCM run + 10-d nudging: prescribed CO2, WT> 10 m
depth nudging with Ishii ocean profile. No nudging within 0-
10 m depth (ECHAM/SIT/TIMCOM)
OAGCM run + no nudging: prescribed CO2
(ECHAM/SIT/TIMCOM)
PobiNN(ECHAM/SIT/TIMCOM) VS Obstsw
Monthly Mean : Seasonal cycle
Chen, 2013
Chen, 2013
OAGCM+ no
nudging
AGCM+1d ocean Hadley SST
wavelet analysis for NINO3.4
( quick result, to be checked…)
Chen, 2013
OAGCM+ 10-d
nudging
Features: Improved air-sea interaction
for MJO and diurnal time scales
Exchange AGCM and OGCM flux/SST with time scale < 40
min.
11 levels within 10-m depth (for recording the information
of ocean warm layer and cool skin)
AGCM and OGCM grids are co-allocated. (reduced the
interpolation needed)
Gasper et al. vertical diffusivity scheme is used. 1.5
TKE+mixing/dissipation length approach
ECHAM/SIT/TIMCOM (EHTW ESM)
SIT (Snow/Ice/Thermocline) Coupler
AtmosphereECHAM (AGCM)19/31 levels(T31-T213)
Ocean:TIMCOM (OGCM)31 Levels (T31-T213/2 deg)
VD
IFF/SIT (Air/Sn
ow
/Ice/Th
erm
oclin
e)
2 sn
ow
+2 ice+
41
water levels
Figure 9 Simulated x-sliced profiles of
water temperature, salinity, current,
heat diffusivity, turbulent kinetic energy,
mixing length in February 1992 at
51.1875E. (Tsuang et al., 2001)
Heat diffusivity increases with
depth, then drop to background
below mixing depth (Gasper et
al., 1990)
Kh=sqrt(TKE)*mixing length
Gasper et al.’s (1990, JGR) vertical diffusivity
Diurnal SST,
x: month(1~12),
y: hour(0~23 LT)
(a) TAO
(b) SIT_T31
WL Tseng (2012)
Ocean-atmosphere interaction: A key element of the Madden-Julian
Oscillation
Wan-Ling Tseng1, Ben-Jei Tsuang2, Noel Keenlyside3, Huang-Hsiung Hsu4, and Chia-Ying Tu4
1. GEOMAR, Helmholtz Centre for Ocean Research Kiel, Kiel, Germany2. National Chung-Hsing University, Taichung, Taiwan3. University of Bergen, Bergen , Norway 4. Academia Sinica, Research Center for Environmental Changes, Taipei, Taiwan
Final climate simulation test: SST and
precipitation!
AMIP run (1945-2010): Hadley SST and seaice (ECHAM)
AGCM+1-D ocean run (1945-2010): prescribed CO2, WT>
10 m depth nudging with Ishii ocean profile. No nudging
within 0-10 m depth (ECHAM/SIT)
OAGCM run (1945-2010): prescribed CO2, WT> 10 m
depth nudging with Ishii ocean profile. No nudging within 0-
10 m depth (ECHAM/SIT/TIMCOM)
Hadley SST
AGCM+1D ocean
(ECHAM/SIT with
10-d nudging)
OAGCM run
(ECHAM/SIT/TIMCOM
with 10-d nudging)
Mean
Difference
DiffRatio
Std.
SST Simulation vs Hadley SST (T106)
Chen, 2013
GPCP AMIP run
(ECHAM)
OAGCM run
(ECHAM/SIT/TIMCOM
with 10-d nudging)
Mean
Difference
DiffRatio
Std.
Precip. Simulation vs GPCP (T106)
Chen, 2013
T63 AGCM AGCM+1d
ocean
OAGCM with
10-d nudging
OAGCM without
nudging
CORR 0.79 0.72 0.81 0.76
SD_Ratio 1.20 0.95 1.10 1.03
SD_EHTW 2.06 1.63 1.89 1.76
SD_GPCP 1.71 1.71 1.71 1.71
RMS 1.64 1.58 1.26 1.45
EHTW ( T63 ) Global Precip. Simulation : relative to the GPCP climatology for 1979-2009
T63T106
T31
Can a coupled OAGCM model do a better weather and
climate forecast than than an AGCM driven by good SST?
No, (AGCM+1D ocean), the bias of the mean SST is too large by
AGCM+1D ocean, the resolved shorter-time scale flux is not
good enough.
Yes, (3-D OAGCM) only the bias from the SST mean state by the
coupled model can be overcomed by a better resolving the air-sea
interaction with time scale short than the mean state.
1. For example, if the rainfall by diurnal cycle and Mudian-Jullian
Oscillation (MJO) can be better resolved by the coupled model.
2. 𝐿𝐸 = 𝑤 ∙ 𝑞 = 𝑤 ∙ 𝑞 + 𝑤′ ∙ 𝑞′𝑚𝑜
+ 𝑤′ ∙ 𝑞′𝑚𝑗𝑜
+ 𝑤′ ∙ 𝑞′ℎ
Extended forecast: > 7d
B.J. Tsuang, Mong-Ming Lu
45 days of 500hPa GPH Anomaly Corr. Coef.
Coupled OAGCM
ECHAM/SIT/TIMCOM
(EHTW ESM), T106
More skill in the tropics: 200 hPa U comp. ACC for Tropical
(20oN~20oS)
(AMIP daily SST)
(AMIP daily SST)
(AGCM+3D Ocean)
(AGCM+1D Ocean)
Conclusion
Diurnal SST and MJO can be better captured by an OAGCM
with better treatment of fine vertical resolution in the upper
10m, co-allocated grid of AGCM and OGCM, exchange data
at ~40 min interval, and good vertical diffusivity scheme.
Precipitation can be better simulated by such an OAGCM
framework than AGCM driven by perfect SST