Download The Earth Climate System Model Development at Academic Sinica

Climate Modeling Activities of
Consortium for Climate Change Study
(CCliCS)
- A Climate System Modeling Initiative in Taiwan
PIs: Huang-Hsiung Hsu, Shaw-Chen Liu, J.-P. Chen, Ming-Dah Chou, P.-L. Lin,
C.-T. Chen, C.R. Wu, Y.-H. Tseng, C.-C. Wu, …
Collaborators: Hua-Lu Pan, S.-J. Lin, K.-N. Liou, Leo Oey, Jin-Yi Yu, C.-C. Chen,
Song-You Hong, H.-P. Huang, John Chiang, W.-K. Tao, …
Academia Sinica
National Taiwan University
National Taiwan Normal University
National Central University
NSC Climate Change Core Project
Consortium for Climate Change Study (CCliCS)
(RCEC/AS, NTU, NCU, NTNU)
Modeling Capacity Building
Period: 2011-2016
Taiwan Climate Change Information Platform (TCCIP)
Taiwan Climate Change
Assessment
Period: 2010-2012, 2013-2015
Climate Change Adaptation Technology (TaiCCAT)
Environmental Monitoring Capacity
Building, Vulnerability Assessment
Period: 2011-2013
氣候變遷研究聯盟
Core Project: Laboratory for Climate Change Research
Subproject 1:Development of cloud and aerosol
parameterizations for climate models
Subproject 2:Development of regional climate
modeling system
Subproject 3:Detection and attribution of the impact
of anthropogenic greenhouse gas
emission and warming on the change
of extreme weather systems
From Global to Local
Models Implementation
and Development:
• CESM
(coarser grid)
 Global climate and East Asian monsoon
Change and variation, natural vs. anthro.
 Parameterization: convection,boundary
layer,land surface, radiation, cloud,…
 Aerosol
 Ocean
• HiRAM
(fine,stretched-grid)
 Detection and attribution studies
 Extreme weather characteristics changes
 Time-slicing climate change simulations: providing highresolution data for dynamical and statistical downscaling
for impact study
• WRF and CReSS
 Detection and attribution studies
 Typhoon and heavy rainfall simulation
 Dynamical downscaling using time-slicing
simulation results
Goals in using the CESM
• More products and more frequent outputs
from the runs to support statistical and
dynamical downscaling efforts for Taiwan and
East Asia.
• Replacing/improving the parameterizations to
generate an independent estimate for climate
change scenarios.
Double ITCZ? alternating ITCZ
Obs.
Coupled-CAM4
CAM5
Coupled-CAM5
Eastern Pacific, April
(b)
Low to middle
levels are too
humid!
Weak Asian Summer Precipitation
Structure and Parameterizations
• Model structure
– higher horizontal (e.g., 0.25 degree lat/lon) and vertical (30, 60, …
levels) resolutions
– A higher top (.1 hPa) may help in simulating effects of tropicalextratropical, troposphere-stratosphere Interactions
• Radiation schemes (e.g., RRTM vs. Chou and Suarez)
• PBL and convection parameterization scheme used in the
NCEP Global Forecast System (GFS) to resolve more synoptic
variability
Deep cumulus
Shallow cumulus
PBL
GFS physics Simplified ArakawaSchubert scheme (
Han & Pan, 2011;
Wu & Pan, 1995)
Mass-flux based +
surface-flux closure
(Han & Pan, 2011)
K profile + non-local
counter-gradient mixing+
cloud-top radiative-driven
entrainment
(Holtslag & Boville, 1993;
Han & Pan, 2011)
CAM5default
physics
UW shallow convection
(Park & Bretherton,
2009; Bretherton et al.,
2004 )
UW moist turbulence
(Bretherton & Park, 2009;
Bretherton et al., 2004)
Zhang-McFarlane
scheme (Neale et al.,
2008; Zhang &
McFarlane, 1995)
Community Atmosphere Model 5 (CAM5; Neale et al. 2010)
• 30 vertical levels
• Default resolution is 1°, but coarser resolution is available.
AMIP type simulations (~2°)
• Simulated for 6 years and latter 4 years for analysis
• Driven by monthly climatological SST of blended HadleyISST1 and OI.v2
NWP-type hindcasts (~1°)
• Initialized and forced by EC analysis during YOTC, similar to Transpose AMIP
Y.-C. Wang, C.-J. Shiu, C.-A. Chen, H.-L. Pan
Global
Distribution of
Annual
Precipitation
GFS
physics
•
CAMdefaultphysics
•
Global rainfall average
– GFS physics : 2.75
mm/day
– CAM5 default : 2.95
mm/day
Observations :
– CMAP
~2.69mm/day
(1979-1998)
– GPCP ~2.67mm/day
(1979-2009)
Difference
Y.-C. Wang
Parameterizations
• We are testing the CESM using a more advanced
microphysics and aerosol package developed in
the National Taiwan University and tested in the
WRF model in an attempt to improve the cloudradiation-aerosol interactions. (J.-P. Chen)
Main foci: cloud, precipitation and aerosol physical processes,
including aerosol‐cloud interaction.
Cloud parameterization: a cumulus parameterization considering
aerosol effect, an explicit scheme resolving moment changes
of various cloud hydrometeors.
Aerosol parameterization: a multi‐modal and multi‐moment
scheme simulating different aerosol types (condensation
and ice nuclei, and mixing state).
Aerosol‐cloud interaction scheme: coupling cloud and aerosol
parameterization so that the effect of condensation nuclei
and ice nuclei on cloud and precipitation formation can be
better represented.
(J.-P. Chen)
Inclusion of warm cloud parameterization in deep convection scheme
Model vs. Model
Total Precipitation Rate Model vs. Observation
CAM5.2+JP2M
CAM5.2+JP2M
CAM5.2 Ctrl
GPCP
JP2M - Ctrl
JP2M - GPCP
(C.-J. Shiu)
Aerosols Treatment in Model
treatments
prescribed
aerosol distribution from other models
bulk
Mass (no size distribution)
No detailed aerosol microphysics
moments (PDF to describe size distribution)
detailed aerosol microphysics
kernel simplification
n(r) (detailed aerosol microphysics)
detailed aerosol microphysics
modal
bin
(I.-C. Tsai, J.-P. Chen)
Statistical-Numerical Aerosol Parameterization (SNAP)
106
dN/dlngD(cm-3)
• Modal approach
• Prognostic formulas:
ice nucleation, condensation,
coagulation, scavenging, dry
deposition
initial
bin
GHQ
BS95
SNAP-C
105
104
103
102
103
3
3
• Diagnostic formulas:
binary nucleation, equilibrium
size, activation cutoff size, modal
absorption/extinction coefficient
dV/dlngD (m /cm )
2
3
dS/dlogD(m /cm )
104
102
101
100
102
101
100
10-1
10-2
0.001
0.01
0.1
1
particle diameter(m)
10
Chen et al. (2013) submitted to ACP
Aerosol Optical Depth
MAM3
Precipitation
SNAP-MAM3
Precipitation: slightly increases in SNAP run
Parallel Domain-Decomposed Taiwan
Multi-Scale Community Model (PD-TIMCOM)
Tseng et al. (2011a), Prog. Ocean (in press)
Tseng and Chien (2011), Comp. Fluids
Shen et al. (2011a), J. Mar. Sys.
Shen et al. (2011b), J. Clim. (submitted)
Young et al. (2011), Env. Modell. Soft. (submit
Impact of 3-D Topography Structure on Surface Radiation
- tested over the Tibetan Plateau and Sierra Nevada
Wei-Liang Lee and K. N. Liou
Deviation in the total net flux
Noon, 3/21
Elevation
Diurnal cycles
of flux deviations
between 1D & 3D
Domain-Average
Downward Flux
3/21
Difference in surface
solar flux at 9:00
Improvements of CLM simulations by implementing
realistic surface/subsurface hydrological parameters and
human dimension parameterizations (Min-Hui Lo)
•
•
•
Some key parameters in the CLM: e.g., groundwater parameters, soil depth, streamflow velocity, and so
on are usually constant globally due to the lack of observed datasets to estimate these parameter
values.
In this project, we propose to use remote sensing datasets, such as Gravity Recovery and Climate
Experiment (GRACE) or Altimetry data, to help estimate the model parameters through multi-objective
parameter estimation framework (Lo et al., 2010).
Example : how different soil depths affect the land water storage seasonality (global average).
How soil depth can affect the climate?
1.4 m soil depth
3.4 m soil depth
A test for different soil depths. The shallower
depth has weaker seasonality for total water
storage anomalies. The use of the realistic
bedrock depth allows a better estimate of
soil moisture memory that affects land
hydrology and, furthermore, the climate.
Development of Stretched-grid HiRAM
- Joint effort with S.-J. Lin at GFDL
C.-Y. Tu, S.-J. Lin
AMIP – Western Pacific Tropical Cyclone Tracks
(C384R25TW/OKC)
Coupling 1-D mixed layer model (SIT) to ECHAM5
Significant improvement in MJO simulation
(joint effort with Noel Keenlyside, B.J. Tsuang)
Can coupling the SIT model have similar magic on CAM?
Using CESM/CAM
for mechanism study
e.g., Influence of the Arakan
mountain using 1-degree res.
CESM
The Arakan mountain is added in the experiment run
using CESM coupled with mixed-layer ocean, and the
8th-17th years of the model output are analyzed.
Unit: m
Climate Process Study
- Mechanisms affecting the 2010 Russian Heat Wave (H.-H. Hsu)
- Thank You for Your Attention -
Global modeling team
Climate Modeling:
• Convection and PBL: H.-L. Pan，C.-J. Shiu，
Post-docs - Y.-C. Wang、C.-A. Chen
• Radiation: M.-D. Chou 、W.-L. Lee、C.-J. Shiu
• Cloud and Aerosol: J.-P. Chen、W.-T. Chen，Post-doc - I.-C. Tsai
• Ocean: C.-R. Wu、Y.-H. Tseng、B.-J. Tsuang、Yi-Chia Hsin，
Post-docs: C.-H. Chow、K.-S. Shie、Y.-H. Wu、Y.-Y. Lan
• Land Surface: M.-H. Lo
• Vertical Structure: NCAR collaborators，Post-doc: T.-L. Lai
• HiRAM: S.-J. Lin、C.-Y. Tu，Post-doc: P.-J. Chiu
Climate and Model Diagnostics:
H.-H. Hsu、Shaw-Chen Liu、C.-C. Wang、S.-Y. Lee
Post-docs: C.-H. Wu、C.-Y. Lin、Anoop Mishra、
Nicolas Freychet、W.-L. Tseng
LCCR/RCEC serves as a research hub of Climate Change Research
in Taiwan
University Training Program on
Climate Change Research
CCliCS
Model
Developme
nt and
Implement
ation
for Climate
Change
Research
Climate
Variabili
ty and
Change
Climate
Simulation
Platform
NCHPC
NSC Research Projects
CWB
TTFRI
TCCIP
TaiCCAT
Laboratory
Climate
Data
platform
Laboratory for Climate
Change Research
Training
program
Modeling and
Data Platforms
Climate Change
and Variation
RCEC-Univ.
Joint Graduate
Program
Detection and
Attribution
Training Course,
Workshop
High-impact
Weather
High-res.
Climate
Projection
Simulation and
Diagnostics of
Global and East
Asian Climate
Model
Implementation
and Development
CESM、HiRAM
Aerosol-cloudclimate
Ocean
Regional
Modeling
International
Collaboration
Visiting
Scientist
Dispatching
Program
Foreign
Collaborating
Institute
http://blc.twbbs.org/c384_olr_1
997-08-ty.gif
Topic: Understand the effect of dust on cloud microphysics
and precipitation in a frontal case via WRF
Simulation info:
Machine: NUWA, 128 CPU
Run time:
6 hrs / 72-hr simulation
d1
d2
d3
resolution
27km
9km
3km
grid
328 x 219
85 x 121
109 x 169
Topic: understand the aerosol-cloud interactions in a
typhoon case by coupled WRF-Chem
Simulation info:
Machine: NUWA, 128 CPU
Run time:
68 hrs / 72-hr simulation
resolution
d1
27 km
d2
9 km
d3
3 km
grid
181*121
274*244
331*331
Parallel Domain-Decomposed Taiwan
Muti-Scale Community Model (PD-TIMCOM)
• Bathymetry form 1-min
etopo1
• Pacanowski and Philander
Vertical mixing (1982)
and Smagorinsky
horizontal mixing (1993).
• Initial Temperature and
Salinity from NOAA
WOA 09.
• Surface wind forcing
from Hellerman and
Rosenstein (1983)
• 1/16°and 1/4° horizontal resolution, latitudes covers from 72°S to 72°N.
with 51 linear exponential levels vertically. (1440x792x51)
• Primitive, hydrostatic equation
• Fourth-order combined Arakawa A and C-grid (1977)
• Rid-lid approx. and Free surface are used (Yang et al., 2012).
Tseng and Chien (2011)
Included low-resolution-run performance
Low resolution (o)
High resolution (*)
Number of cores used
v.s. executed time:
Linear trend
Resolution
Exec. Time
2-deg. 1.5 hr.
1-deg. 4 hr.
0.25-deg. 31 hr.
(Based on 12 cores)
Comparison with Observation
Thompson and Demirov (2006)
Benchmark of HiRAM on ALPS
(Rank138@Top500, Nov.2012)
機器名稱
NCHPC
ALPS
Academia
Sinica
NUWA
AMD Opteron 6174,
12 cores, 2.2GHz
AMD Opteron 6136,
8 cores, 2.4GHz
Intel Xeon QuadCore 5450,
8 cores,3.0GHz
Intel Xeon 6Core 5450,
12 cores,3.0GHz
可用計算資源
(nodes,CPU,cores)
(80,320,3840)
(124,248,1984)
(48,96,1152)
儲存空間
(硬碟,雲端)
(40TB ,200TB)
(720TB ,n/a)
 (2.5PB, n/a)
(100TB , n/a)
編譯器
GNU,Intel,PGI
GNU,Intel,PGI
GNU,Intel,PGI
Platform MPI、
OpenMPI、
MVAPICH、
MVAPICH2
Platform MPI、
OpenMPI、
MVAPICH、
MVAPICH2
Platform MPI、
OpenMPI、
MVAPICH、
MVAPICH2
項目
CPU
MPI
資料分析軟體
NCDR
TCCIP
NCL、Matlab、
NCL、Matlab、
NCL、Matlab、
Fortran、CDO etc. Fortran、CDO etc. Fortran、CDO etc.
Observation
CCliCS CESM1
Output
CMIP5 Data
Data type
Time Period
Frequency
TRMM
SSMI
NOAA
JRA25
ERA40
ERA-Interim
NCEP-R1
NCEP-R2
NCEP-20C
NCEP-CFSR
NASA MMF
NASA_MERRA
YOTC
f19.F_AMIP_C5
f19_g16.B1850-2000C5
f19_g16.B1850_CAM5
f09_g16.CAM5.B1850-2000
f09_g16.CAM5.B1850CN
f09_g16.CAM5.B1850CN.lev60
f19_g16.B1850_CAM5
lev30.f09.E1850C5.01
lev60.f09.E1850C5.01
f02_t12.B1850CN.CAM5
1998-2010
1987-2011
1974-2011
1979-2001
1958-2002
1979-2012
1948-2011
1979-2011
1871-2008
1979-2009
20060801-20060805
1979-2011
200905-201003
1979-1988
1850-2004
90 years
1863-2005
232 years
63 years
100 years
40 years
46 years
3 years
Daily
Monthly , Daily
Monthly , Daily
Monthly , Daily
Monthly , 6-hourly
Monthly , 6-hourly
Monthly , 6-hourly
Monthly , 6-hourly
Monthly , 6-hourly
Monthly , 6-hourly
3-hourly
Monthly , Daily
6-hourly
Monthly
Monthly
Monthly
Monthly
Monthly
Monthly
Monthly
Daily
Daily
Hourly
Models
Experiment
piControl
historical
historicalExt
historicalGHG
historicalNat
historicalMisc
RCP26
RCP45
RCP60
RCP85
Frequency
CCSM4
CNRM-CM5
CSIRO-MK3-6-0
CanESM2
FGOALS-g2
FGOALS-s2
GFDL-ESM2G
GFDL-ESM2M
GISS-E2-R
HadGEM2-CC
HadGEM2-ES
IPSL-CM5A-LR
MIROC-ESM-CHEM
MIROC5
bcc-csm1-1
gfdl-cm3
inmcm4
miroc-esm
model access1-0
model ec-earth
model ipsl-cm5a-mr
CESM-CAM5
CESM-bgc
CESM-fastchem
CESM-Waccm
GFDL-HIRAM-C360
MPI-ESM-LR
MRI-CGCM3
NorESM1-M
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Monthly ,
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Daily
Climate Variability and Change:
Using models and obs./simulated data to
study • Global climate and East Asian monsoon
Changes
• Natural climate variability vs.
anthropogenic climate change
• Impacts on extreme weather
Research Platform:
• Climate Simulation Platform: providing model
suites and computing resources
• Climate Data Platform: providing longterm simulation data
Development of Stretched-grid HiRAM
- Joint effort with S.-J. Lin at GFDL
for high-resolution Time-Slicing Experiments
Present, near future, end of century  TCCIP and TaiCATT
 Cube Sphere Globe (HiRAM)
• Defined by intersects of great circles with
equal-distance along 12 edges
• Maximum local grid aspect ratio ~ 1.061
• Maximum global grid aspect ratio ~ 1.414
Resolution:
C2000,
x = ~ 4.5 km
C384 ,
x = ~ 23 km
C384R2.5 ,
x = 10~62 km
Time-slicing experiments using HiRAM
C384, 1,536 CPU: one year/37.5 hrs
30-year AMIP simulations: present, near future, end-ofcentury (about 1.5 months for each simulation)
Collaboration with MRI using same SST
國網中心御風者資源：
CPU
AMD Opteron 12 Core 2200 MHz
Memory
96 GB (2 GB/ core)
Networking
QDR InfiniBand
運算效能：
計算核心數
(48 cores / node)
1536
執行效率
(model year / day)
0.64
 Stretched Cube Sphere Globe
• Grid number for stretched and
unstretched are the same.
• ΔT (time-step) is determined
according to the finest grid
size.
38
4
grids
384 grids
Stretched Cube Sphere Globe (center @ TW)
C384 Stretched Factor 2.5 (Δx ≈ 10km~62km)
5-Yr NH Tropical Cyclone Tracks
(center @ OKC/TW)
293 (17.5 m/s)
161 (32.5 m/s)
231 (17.5 m/s)
101 (32.5 m/s)
Precipitation – PRISM v.s. HiRAM C384R2.5 OKC
C384R2.5 OKC
C.-Y. Tu, S.-J. Lin
Computing & Storage Demand
(ALPS: 177 T-Flops, 25,600 CPUs )
1-year simulation
on ALPS
C384
ΔT=600s
C384R2.5
ΔT=300s
96 CPUs
499.6 hrs
1,334.8 hrs
( 2.67x longer than C384 )
384 CPUs
87.6 hrs
------
( 5.7x faster than 96 CPUs)
output
31.7GB
31.7GB
restart
8GB
8GB
Impact of a 1-D ocean mixed layer model
to MJO simulation using ECHAM5
Uncoupled:
prescribed SST
experiments
Coupled
experiments:
- Fine vertical
resolution (1m in
upper 10m)
- Coarse vertical
resolution
AGCM: ECHAM5.4 (T63L31)
25 years
SST
0.005
1
2
3
4
5
6
SIT: One column
ocean model
Tsuang and Tu 2005
8
Solves: T, U, S
using TKE scheme
9
(Gasper et al. 1990)
7
10
(m)
Nudging to climatology SST every month
Observation
Effects of land use changes on surface energy
budget and land sea breeze circulation in Taiwan
National Central University
In western Taiwan, the country
is classified as irrigated
cropland.
The urban area is overly
urbanized.
The major cities are
accurately identified.
Observation
25.2%
17.7%
Considering the possibility …
CESM components : all active components, pre-industrial, with CN
(Carbon Nitrogen) in CLM
Los Alamos Sea Ice Model
version 4.0 (CICE)
Community Land Model
version 3.0 (CLM3)
CLM
CICE
CPL7
Coupler version 7.0
CAM
TaIwan Multi-scale
Parallel Ocean Program
Community Ocean Model
version 2.0 (POP)
(TIMCOM, Y.-H. Tseng)
TIMCOM
POP2
Community Atmosphere Model
version 5 (CAM) featuring finite
volume dynamical core