Presentation - Computational Information Systems

Transcription

Climate Modeling at the
Center for Ocean-LandAtmosphere Studies
Jim Kinter, COLA
Center of Ocean-LandAtmosphere Studies
Computing in the Atmospheric Sciences
Annecy, France
September
Jim Kinter 14
- Computing
in the Atmospheric2009
Sciences || Annecy, France || 14 September 2009
Institute of Global Environment and Society (IGES)
IGES
Board
Board of
of Directors
Directors
President:
President: J.
J. Shukla
Shukla
Executive
Committee
Scientific
Adv. Comm.
Business
Office
COLA
Information
Systems
CREW
Center
-LandOcean
Land-Atmosphere
Center for
for OceanOcean-Land-Atmosphere
Studies
Studies (established
(established 1993)
1993)
Director:
J.
Kinter
(2005)
Director: J. Kinter (2005)
Center
Center for
for Research
Research on
on Environment
Environment
and
and Water
Water (established
(established 2005)
2005)
Director:
L.
Gates
(2009)
Director: L. Gates (2009)
JAMES
JAMES
Journal
Journal of
of Advances
Advances in
in Modeling
Modeling of
of Earth
Earth Systems
Systems
Jim Kinter - Computing in the Atmospheric Sciences || Annecy, France || 14 September 2009
http://adv-model-earth-syst.org/
Institute of Global Environment and Society (IGES)
IGES
Board
Board of
of Directors
Directors
President:
President: J.
J. Shukla
Shukla
Executive
Committee
Scientific
Adv. Comm.
Business
Office
COLA
Information
Systems
CREW
Center
-LandOcean
Land-Atmosphere
Center for
for OceanOcean-Land-Atmosphere
Studies
Studies (established
(established 1993)
1993)
Director:
J.
Kinter
(2005)
Director: J. Kinter (2005)
Center
Center for
for Research
Research on
on Environment
Environment
and
and Water
Water (established
(established 2005)
2005)
Director:
L.
Gates
(2009)
Director: L. Gates (2009)
JAMES
JAMES
Journal
Journal of
of Advances
Advances in
in Modeling
Modeling of
of Earth
Earth Systems
Systems
Vision and Mission
VISION
Global Society Benefits
from Basic and Applied Research and Education
on Climate Variability and Predictability
and the Free Access to Data and Research Tools
MISSION
Explore, Establish and Quantify
the Predictability and Prediction
of Sub-Seasonal to Decadal Variability
in a Changing Climate
“Omnibus” Funding
COLA is supported by NSF (lead), NOAA and NASA through a single
jointly-peer-reviewed*, jointly-funded five-year proposal.
* Thanks to our peers and the agencies
2009-2014
Predictability of the Physical Climate System
(awarded 9/1/2009)
Funding: ~$4M / yr (NSF - 52%; NOAA - 36%; NASA - 12%)
Principal Investigator: Kinter
Co-Investigators:
DelSole, Dirmeyer, Huang, Kirtman, Klinger,
Krishnamurthy, Lu, Schneider, Shukla, Straus
2004-2008
Predictability of Earth’s Climate
Funding: ~$3.25M / yr (NSF - 46%; NOAA - 39%; NASA - 15%)
Principal Investigator: Shukla
Co-Investigators:
DelSole, Dirmeyer, Huang, Kinter, Kirtman, Klinger,
Krishnamurthy, Misra, Schneider, Schopf, Straus
1999-2003
Predictability and Variability of the Present Climate
Funding: ~$2.75M / yr
Principal Investigator:
Co-PIs:
Co-investigators:
1994-1998
Predictability and Variability of the Present Climate
Funding: $2.25M /yr
Principal Investigator:
Co-PIs:
J. Shukla
J. Kinter, E. Schneider, P. Schopf, D. Straus
P. Dirmeyer, B. Huang, B. Kirtman
J. Shukla
J. Kinter, E. Schneider, D. Straus
Core Staff
(1983-1993: University of Maryland; 1993-present: IGES)
Scientist
J. L. Kinter III *
Highest Degree
Ph.D. Princeton (1984)
Joined
1984
Dir. COLA (2005-present); Exec. Dir. COLA (1993-2004)
T. DelSole *
P. A. Dirmeyer
B. E. Doty *
M. J. Fennessy
B. Huang *
V. Krishnamurthy *
J. Lu *
L. Marx
D. A. Paolino
E. K. Schneider *
J. Shukla *
D. M. Straus *
Ph.D. Harvard (1993)
1997
Ph.D. Maryland (1992)
1991
B.S. N. Illinois (1978)
1984
M.S. SUNY-Albany (1980)
1984
Ph.D. Maryland (1992)
1991
Ph.D. M.I.T. (1985)
1983
Ph.D. Dalhousie (2003)
2008
M.S. MIT (1977)
1983
M.S. Illinois (1980)
1984
Ph.D. Harvard (1976)
1984
Sc.D. MIT (1976); Ph.D. BHU (1971)
1983
Pres., IGES (1993-present); Dir. COLA (1993-2004)
Ph.D. Cornell (1977)
1983
* also affiliated with George Mason University
Your most precious
possessions are the people
you have working there, and
what they carry around in
their heads, and their ability
to work together.
- Robert Reich
Scientific Staff
Scientist
D. Achuthavarier
B. Cash
E. Jin *
Z. Guo
Z. Hu
B. Klinger *
J. Lu *
J. Manganello
X. Pan
C. Stan
J. Wei
R. Wu
X. Yang
L. Zhang
Highest Degree
Joined
Ph.D. George Mason (2009)
Ph.D. Penn State (2000)
Ph.D. Seoul National (2005)
Ph.D. Ohio State (2002)
Ph.D. Beijing (1991)
Ph.D. MIT-WHOI (1992)
Ph.D. Dalhousie (2003)
Ph.D. GMU (2004)
Ph.D. George Mason (2009)
Ph.D. Colorado State (2004)
Ph.D. Georgia Tech (2007)
Ph.D. Hawaii (1999)
Ph.D. SUNY-Albany (2006)
Ph.D. Texas A&M (2005)
2009
2002
2006
2002
2000
2000
2008
2006
2009
2005
2007
2002
2006
2007
* also affiliated with George Mason University
Your most precious
to work together.
- Robert Reich
Technical and IT Staff
Scientist
J. Adams
E. Altshuler
C. Steinmetz, Dir.
T. Wakefield
Highest Degree
M.S. Washington (1993)
M.S. Maryland (1996)
Ph.D. Purdue (1991)
B.S. Maryland (2004)
Joined
1999
1998
1998
2000
Your most precious
to work together.
- Robert Reich
GrADS and GDS
•
GrADS has O(104) users worldwide with O(104) of copies of the S/W
downloaded
•
GrADS figures are frequently found in weather and climate journals, e.g. up
to a third of J. Climate graphics
•
GrADS is used to generate figures on dozens of NOAA, NASA, university
and non-US weather and climate data web pages
(www.iges.org/grads/gotw.html)
•
GDS serves thousands of unique users (millions of hits) monthly from
NOAA/NOMADS, NASA/LIS, CEOP etc.
•
COLA GDS (including NCAR dataportal) 2003-2007:
– > 100 million hits (80 million data requests)
– > 4.5 TB sent
– Averaging > 1,200 unique IPs/month
2005 Edition
Figures made with GrADS
Courtesy of Jennifer Adams, COLA
COLA Computing
Facility
Lustre
Filesystem
1.8 M “compute” CPU-hrs/y
~80 TB online storage
80 TB
Storage Network (switched 1Gb/s)
Infiniband 10Gb/s
Compute
Cluster
Compute
Nodes
Login/Analysis
Nodes
176 CPUs
64 CPUs
10 CPUs
Tape
Archival
Desktop Network (switched 100Mb/s)
Desktops
Network
Printing
World Wide Web
Web
Services
~90 GB/mo
I2
&
T1 >1 Gb/s capacity
NCAR
SCD
Community
Computing
NCAR
SCD
Climate
Simulation Lab
NSF TeraGrid
NASA Ames
NAS
Facility
0.8 M CPU-hrs/y
1.8 M CPU-hrs/y
35 M CPU-hrs
2.3 M CPU-hrs/y
Balancing Future Demands on Computing Power
1/120
Resolution
EO, Data Assimilation
Computing
Resources
Dur
ity
x
e
l
p
C om
atio
na
nd/
or E
n
sem
ble
size
Predictability of the
Physical Climate System
Overarching Scientific Questions
What limits predictability at all time scales from days to decades? Is there a
fundamental limit? What is the role of model error? Initial conditions error?
What contributes to predictability at different time scales in the initial state, the
coupling of system components, and the changes in external forcing?
What aspects of the total climate system (global troposphere, stratosphere, world
oceans, sea ice, land surface state, vegetation, snow) are predictable in which
geographic regions, for which seasons, and how does that change in the future?
For the current generation of climate models and observing systems? Future
generations?
What is the optimal combination of models to predict means? Extremes?
Current models have huge limitations, e.g. for regional water cycle ⇒ need to develop a multimodel ensemble combination that produces the best forecast
Representative Projects
Omnibus: I-S-I Predictability Studies with CCSM and CFS (NCAR CSL)
PetaApps: Climate Models Representation of Unpredictable Noise in the
Atmosphere, Ocean or Sea Ice (TeraGrid Ranger and Kraken)
Multiscale Modeling Framework: Superparameterization Cloud-System Resolving
Model for Coupled Climate Simulation (Collaboration with CSU CMMAP; NCAR
Community Computing)
Project Athena: An International, Dedicated High-End Computing Project to
Revolutionize Climate Modeling (Dedicated XT4 at NICS)
Omnibus: I-S-I Predictability Studies with CCSM and CFS (NCAR CSL)
Multi-Model with US National Models
Institutional Collaborations
•
Community Climate System Model (CCSM; NSF)
– Leading effort to correct tropical biases
– Adaptation as seasonal predictability research tool
•
Climate Forecast System (CFS; NOAA - Climate Test Bed)
– Participation in external and internal CTB planning
– Predictability research with NCEP CFS
– Work toward multi-model prediction capability
•
Modeling, Analysis and Prediction (MAP) (GEOS; NASA)
– New ESMF-based model (GEOS-5) in multi-model ensemble
– Utilizing NASA satellite data for predictability and prediction research focused on
characterizing role of noise and initialization
Pacemaker Strategy:
Overcoming Shortcomings of Coupled Models
Observed
Observed
Pacemaker
JJA Rainfall Composite (El Nino - La Nina)
Pacemaker design:
specified SST regions
Pacemaker
DJF SST Composite (El Nino - La Nina)
The “pacemaker” strategy, recently adopted by the International C20C Project, permits a consistent air-sea energy balance
while simultaneously including the time sequence of climate-driver events, such as ENSO.
Teleconnections from the eastern tropical Pacific to remote tropical and extratropical regions are well represented in
pacemaker runs, e.g., phenomena that are at once driven by and independent of ENSO, like the Asian monsoon.
Cash et al. 2007
CCSM3.0 Jan 1982 IC
CFS Jan 1982 IC
Courtesy of Ben Kirtman
CCSM3.0 Jan 1988 IC
CFS Jan 1988 IC
Courtesy of Ben Kirtman
Land-Atmosphere
Interactions over
the Great Plains
a) Coupling strength from Koster, Dirmeyer, Guo et
al. (2004) showing “hotspots” for land-atmosphere
coupling
b) Estimate of “GLACE diagnostic”* from 12 land
surface models (Guo et al. 2007)
c) COLA GCM (10-year integration with specified
observed SST) anomaly correlation of Ts
(horizontal scale) and change in correlation when
observed vegetation properties are specified
(vertical scale; Gao et al. 2007)
* Evaporation variability times a land-atmospheric
flux connection function based on the tightness of
the dependence of surface fluxes on soil moisture
CCSM Re-Forecasts with Land ICs
Influence of Systematic Error on CFS Forecast Skill
NINO3: Warm minus Cold composite
CORR. with respect to lead month
based on 1st SEOF mode of SST
Correlation
SST anomalies
1
0.9
0.8
0.7
(Hindcast composite)
Observation
CFS long run
¾ Warm composite (82/83, 86/87, 91/92, 97/98) Cold composite (84/85, 88/89, 98/99, 99/00)
¾ Dashed lines denote composite for Hindcasts
at different lead times
1
2
3
4
5
6
7
8
9
Forecast lead month
Correlation between 1st PCs based on
observation and hindcasts at different lead
times
Correlation between 1st PCs based on long
run and hindcasts at different lead times
PetaApps: Climate Models Representation of Unpredictable Noise in the
Atmosphere, Ocean or Sea Ice (TeraGrid Ranger and Kraken - Cray XT-5)
Peta-Apps Team
•
•
•
•
•
•
•
•
•
•
•
•
•
Ahearn, NCAR
Bitz, U. Washington
Bryan, NCAR (interested party)
Collins, UC Berkeley (co-PI)
Dennis, NCAR
Kinter, COLA (PI)
Kirtman, U. Miami (co-PI)
Loft, NCAR (co-PI)
Min, U. Miami
Nolan, UC Berkeley (grad student)
Siquiera, U. Miami (grad student)
Stan, COLA
Yelick, LBL (co-PI)
Peta-Apps Hypotheses
• Goal: Quantifying/reducing uncertainty from:
– Initial conditions
– Model physics
• Extending the interactive ensemble
– Atmospheric noise
• High-res IE test - qualitatively different?
• Synthetic noise from external source (high-res AMIP)
– Noise in other climate system components
• Ocean
• Land
• Sea ice
– Stochastic physics
Ensemble of N
AGCMs all
receive same
SST(x,y,t)
AGCM1
AGCM2
Response1
Response2
AGCMN
•••
ResponseN
Ensemble Mean Response
Average N
members’
responses
Observed SST
AMIP/GOGA Ensemble
AGCM1
AGCM2
Sfc Fluxes1
Sfc Fluxes2
•••
AGCMN
Sfc FluxesN
SST1
SST2
SSTN
OGCM1
OGCM2
OGCMN
Coupled Model Ensemble
Ensemble of N
AGCMs all
receive same
OGCM-output
SST each day
AGCM1
AGCM2
Sfc Fluxes1
Sfc Fluxes2
AGCMN
•••
Sfc FluxesN
average (1, …, N)
Average N
members’ surface
fluxes each day
Ensemble Mean Sfc Fluxes
OGCM receives
ensemble average of
AGCM output fluxes
each day
SST
OGCM
Interactive Ensemble
Ensemble of N
AGCMs all
receive same
OGCM-output
SST each day
AGCM1
AGCM2
Sfc Fluxes1
Sfc Fluxes2
•••
AGCMN
Sfc FluxesN
rand (1, …, N)
Randomly select
surface fluxes from 1
member each day
Selected Member’s Sfc Fluxes
OGCM receives
output of single,
randomly-selected
AGCM each day
SST
OGCM
Random Interactive Ensemble
PetaApps
NSF PetaApps proposal funded - three-year research and development
effort, aimed at enabling a broad climate science capability for petascale
systems
• Interactive ensembles using CCSM/CPL7
• Incorporate and examine use of PGAS language (Titanium) in CCSM
time
Driver
CAM
CAM
CAM
CAM
CLM
CICE
POP
Interactive ensembles will
be used to understand
how oceanic and
atmospheric weather
noise impacts climate
variability
Titanium will be used in the
flux coupler to examine
impact of PGAS language
approach to performance and
memory footprint
processors
Courtesy Mariana Vertenstein, NCAR
0.5°
0.1°
Courtesy John Dennis, NCAR
Multiscale Modeling Framework: Superparameterization Cloud-System Resolving
Model for Coupled Climate Simulation (Collaboration with CSU CMMAP; NCAR
Community Computing)
Accuracy / Resolution
Issues in the Atmosphere
Convective parameterization - when does it help? hurt?
Convective parameterization tends to reduce strength of
tropical storms, cloud time scale is key parameter; may be
better off without CU parameterization (GMAO finding)
35-km resolution needed to get seasonal cycle of tropical
storm frequency (NCEP finding)
Crossover resolution at which no CU parameterization is
better may be ~ 20 km (GFDL hypothesis)
Evidence that cloud-resolving models are qualitatively
different …
50-km or even 25-km resolution is needed to get statistics
of extratropical winter storms right (ECMWF finding)
17-km resolution may be required to get important extreme
events in extratropics right (ECMWF finding)
Today, scalable dycores can’t break
1 simulated year per day even at 12.5 km…
Full CAM Physics/HOMME Dycore on Blue Gene/L
Parallel I/O library used for physics aerosol input and input data
( work COULD NOT have been done without Parallel IO)
Work underway to couple to other CCSM components
5 years/day
1 year/day
Courtesy Mark Taylor, SNL
What is MMF*?
AGCM domain
CRM domain
TOA
N
ΔXCRM
E
ΔXAGCM, ΔYAGCM
* M. Khairoutdinov and D. Randall
Model Configurations
SP-CCSM
Ctl-CCSM HR-CCSM
(PetaApps Proj)
1
Horiz. Res.
T42 (sld1)
T42 (sld1)
0.47x0.63 (fv2)
Vert. levels
30
26
26
Deep conv.
CRM3
ZM
ZM_New4
Shallow conv.
CRM3
Hack
Hack
Horiz. Res.
gx3v5
gx3v5
tx0.1v2
Vert. levels
25
25
40
Ice model
CSIM4
CSIM4
CICE4.0
Land model
CLM3.0
CLM3.0
CLM3.5
semi-Lagrangian dynamical core
2 finite-volume
dynamical core
3
M. Khairoutdinov and D. Randall
4
Neale et al., 2008
ENSO Simulation
Nino 3 (5S–5N,150W–90W)
SP-CCSM
(0002–0023)
NCEP Rean.
(1979–2001)
Ctl-CCSM
(0002–0023)
ENSO Simulation
NINO3.4 (5S-5N,170W-120W)
48 months
30 months
24 months
Courtesy Cristiana Stan, COLA
SSTA Regression with NINO3.4
SP-CCSM
(0002–0023)
HadlSST
(1948–1998)
Ctl-CCSM
(0002–0023)
ENSO-Monsoon Relationship
IMR (JJA), SSTA(DJF+1)
SP-CCSM
(0002–0023)
HadlSST/
GPCP
(1951–2004)
Ctl-CCSM
(0002–0023)
MJO Simulation
Precipitation
Ctl-CCSM
GPCP
SP-CCSM
courtesy of C. DeMott
MJO Simulation
Phase composites of the OLR dominant MJO mode
(models: 0004–0023; NOAA: 1979–2007)
Ctl-CCSM
NOAA
SP-CCSM
W/m2
6
4.5
3
1.5
0
-1.5
-3
-4.5
-6
courtesy of
V. Krishnamurthy
Project Athena: An International, Dedicated High-End Computing Project to
Revolutionize Climate Modeling (Dedicated Cray XT-4 at NICS)
Collaborating Groups
•
•
•
•
•
COLA - Center for Ocean-Land-Atmosphere Studies, USA
ECMWF - European Center for Medium-range Weather Forecasts, UK
FRCGC - Frontier Research Center for Global Change of the Japan
Agency for Marine-Earth Science and Technology (JAMSTEC), Japan
University of Tokyo, Japan
National Institute for Computational Studies (NICS), USA
Codes
•
•
NICAM:
IFS:
Nonhydrostatic Icosahedral Atmospheric Model
ECMWF Integrated Forecast System
Science Goals
•
Increasing weather and climate model resolution to accurately
resolve mesoscale phenomena in the atmosphere and ocean
can dramatically improve the fidelity of the models in simulating
the mean climate, the distribution of variances and covariances,
and the representation of extreme events.
•
Simulating the effect of increased greenhouse gases on regional
aspects of climate, such as precipitation and storminess, may,
for some regions, depend critically on the resolution of the
underlying climate model.
•
Explicitly resolving important processes in the atmosphere and
ocean, without parameterization, can even further improve the
fidelity of the models, especially in describing the regional
structure of weather and climate.
NICAM - Global Cloud-System
Resolving Model
•
•
QuickTime™ and a
YUV420 codec decompressor
are needed to see this picture.
http://nicam.jp/hiki/?About+NICAM
Real-time simulation of a
Madden-Julian Oscillation event
with NICAM at 3.5 km horizontal
resolution
It is the only global atmospheric
model capable of resolving
clouds
3.5km = 10x211+2 grid points
Greenland Tip Jet Events:
ECMWF D+1 Forecasts at Various Resolutions
TL95L91
TL255L91
(a) SLP and Turbulent Heat Fluxes: 20041226 12z FC+24h (T95)
TL799L91
(b) SLP and Turbulent Heat Fluxes: 20041226 12z FC+24h (T255)
1250
1000
900
900
900
800
800
800
2004
00
10
1250
1000
1000
970
700
700
970
600
500
980
500
990
400
990
400
1000
300
1000
300
200
101
0
102
0
600
980
102
0
100
1010
200
102
100
1250
1000
1000
900
900
900
800
800
700
700
700
600
600
980
990
400
10
10
1020
(f) SLP and Turbulent Heat Fluxes: 20050116 12z FC+24h (T799)
1250
500
1010
100
1000
70
1000
1010
1250
960
960
9
0
17 km
(e) SLP and Turbulent Heat Fluxes: 20050116 12z FC+24h (T255)
800
2005
400
300
1000
50 km
(d) SLP and Turbulent Heat Fluxes: 20050116 12z FC+24h (T95)
500
990
200
140 km
10
30
700
970
600
980
(c) SLP and Turbulent Heat Fluxes: 20041226 12z FC+24h (T799)
1250
0
97
980
990
1000
300
200
100
10
30
102
0
600
0
96
500
980
400
300
200
1010
100
0
97
10
30
10
20
500
400
990
300
1000
200
1010
100
Jung and Rhines, JAS
Process-Resolving Models
•
NSF Petascale Applications (PetaApps) project
–
–
–
–
•
Project Athena
–
–
–
–
•
COLA
NCAR
U. Miami
UC Berkeley
COLA
ECMWF
JAMSTEC and U. Tokyo
NICS
PRAC: Blue Waters
–
–
–
–
COLA
CSU
NCAR
U. Miami
Working on Climate Scalability
Requires Big Interdisciplinary Teams
•
Contributors:
D. Bader (ORNL)
D. Bailey (NCAR)
C. Bitz (U Washington)
F. Bryan (NCAR)
T. Craig (NCAR)
A. St. Cyr (NCAR)
C. Demott (CSU)
J. Dennis (NCAR)
J. Edwards (IBM)
B. Fox-Kemper (MIT,CU)
E. Hunke (LANL)
B. Kadlec (CU)
D. Ivanova (LLNL)
E. Jedlicka (ANL)
E. Jessup (CU)
R. Jacob (ANL)
P. Jones (LANL)
J. Kinter (COLA)
•
S. Mishra (NCAR)
S. Peacock (NCAR)
K. Lindsay (NCAR)
W. Lipscomb (LANL)
R. Loy (ANL)
J. Michalakes (NCAR)
A. Mirin (LLNL)
M. Maltrud (LANL)
J. McClean (LLNL)
R. Nair (NCAR)
M. Norman (NCSU)
T. Qian (NCAR)
D. Randall (CSU)
C. Stan (COLA)
M. Taylor (SNL)
H. Tufo (NCAR)
M. Vertenstein (NCAR)
P. Worley (ORNL)
M. Zhang (SUNYSB)
Funding:
–
•
•
•
•
–
–
–
–
•
DE-FC03-97ER62402
DE-PS02-07ER07-06
DE-FC02-07ER64340
B&R KP1206000
DOE-ASCR
•
B&R KJ0101030
NSF Cooperative Grant NSF01
NSF PetaApps
NSF PRAC
Computer Time:
–
–
–
DOE-BER CCPP Program Grant
Blue Gene/L time:
NSF MRI Grant
NCAR
University of Colorado
IBM (SUR) program
BGW Consortium Days
IBM research (Watson)
LLNL
Stony Brook & BNL
CRAY XT time:
NICS/ORNL
NERSC
Sandia
BlueWaters IBM P7 time??
Scale of the Enterprise
•
Computational Power – Petaflops/sec
•
Data Volumes – Petabytes Exabytes
•
Model re-coding for massively parallel architectures → O(100)
dedicated person-years
•
Climate data assimilation, initialisation and reanalysis → O(100)
dedicated person-years
•
Sufficient expertise in climate and Earth system processes
Challenge will be securing critical capability
in all these areas.
Is it beyond the capacity of a single country?
Is it time to tackle this together and avoid duplication of
effort?
Courtesy of Julia Slingo
World Modeling Summit, May 2008, Reading, UK

Presentation - Computational Information Systems

Transcription

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