Transcript The North American Regional Climate Change Assessment

The North American Regional Climate
Change Assessment Program
(NARCCAP)
Linda O. Mearns
National Center for Atmospheric Research
SAMSI Climate Change Workshop
Research Triangle Park, NC
February 18, 2010
Global Climate Models
Regional Climate Models
What high resolution is
really good for
• In certain specific contexts, provides
insights on realistic climate response to
high resolution forcing (e.g. mountains,
complex coast lines)
• For coupling climate models to other
models that require high resolution (e.g.
air quality models – for air pollution
studies)
Advantages of higher
resolution
North America at typical global
climate model resolution
Hadley Centre AOGCM (HadCM3),
2.5˚ (lat) x 3.75˚ (lon), ~ 280 km
North America at 50 km
grid spacing
Putting Spatial Resolution in the
Context of Other Uncertainties
• Must consider the other major uncertainties
regarding future climate in addition to the
issue of spatial scale – what is the relative
importance of uncertainty due to spatial
scale?
• These include:
– Specifying alternative future emissions of
ghgs and aerosols
– Modeling the global climate response to
the forcings (i.e., differences among
AOGCMs)
Regional Modeling Strategy
Nested regional modeling technique
• Global model provides:
– initial conditions – soil moisture, sea surface
temperatures, sea ice
– lateral meteorological conditions (temperature,
pressure, humidity) every 6-8 hours.
– Large scale response to forcing (100s kms)
• Regional model provides finer scale (10s km)
response
The North American Regional Climate Change
Assessment Program (NARCCAP)
Initiated in 2006, it is an international program that will serve
the climate scenario needs of the United States, Canada, and
northern Mexico.
•Exploration of multiple uncertainties in regional
model and global climate model regional projections
•Development of multiple high resolution regional
climate scenarios for use in impacts assessments.
•Further evaluation of regional model performance over North America
•Exploration of some remaining uncertainties in regional climate modeling
(e.g., importance of compatibility of physics in nesting and nested models)
•Program has been funded by NOAA-OGP, NSF, DOE, USEPA-ORD – 4-year
program
www.narccap.ucar.edu
NARCCAP - Team
Linda O. Mearns, NCAR
Ray Arritt, Iowa State; Dave Bader, LLNL and
Oakridge National Lab; Melissa Bukovsky, NCAR;
Richard Jones, Wilfran Moufouma-Okia, UK Hadley
Centre; Sébastien Biner, Daniel Caya, Ouranos
(Quebec); Phil Duffy, Climate Central; Dave Flory,
Iowa State; William Gutowski, Iowa State; Isaac Held,
GFDL; Bill Kuo, NCAR; René Laprise, UQAM; Ruby
Leung, Yun Qian, PNNL; Larry McDaniel, Seth
McGinnis, Don Middleton, NCAR; Ana Nunes, Scripps;
Doug Nychka, NCAR, John Roads*, Scripps; Steve
Sain, NCAR; Lisa Sloan, Mark Snyder, UC Santa Cruz,
Gene Takle, Iowa State
* Deceased June 2008
NARCCAP Domain
Organization of Program
•
Phase I: 25-year RCM simulations using NCEP-Reanalysis
boundary conditions (1979—2004)
•
Phase II: Climate Change Simulations
– Phase IIa: RCM runs (50 km res.) nested in AOGCMs current
and future
– Phase IIb: Time-slice experiments at 50 km res. (GFDL and
NCAR CAM3). For comparison with RCM runs.
•
Quantification of uncertainty at regional scales – probabilistic
approaches
•
Scenario formation and provision to impacts community led by
NCAR.
•
Opportunity for double nesting (over specific regions) to include
participation of other RCM groups (e.g., for NOAA OGP RISAs,
CEC, New York Climate and Health Project, U. Nebraska).
Phase I
• All 6 RCMs have completed the reanalysis-driven
runs (RegCM3, WRF, CRCM, ECPC RSM, MM5,
HadRM3)
• Results are shown here for 1980-2004 from six
RCMs
• Configuration:
– common North America domain (some differences due
to horizontal coordinates)
– horizontal grid spacing 50 km
– boundary data from NCEP/DOE Reanalysis 2
– boundaries, SST and sea ice updated every 6 hours
Regions Analyzed
Boreal
forest
Pacific
coast
Maritimes
Great Lakes
Upper
Mississippi
River
California
coast
Deep
South
Coastal California
• Mediterranean climate: wet winters and
very dry summers (Koeppen types Csa,
Csb).
– More Mediterranean than the
Mediterranean Sea region.
• ENSO can have strong effects on
interannual variability of precipitation.
R. Arritt
Monthly time series of precipitation in
coastal California
15
mm/day
12
9
6
1982-83
El Nino
RCM3
ECPC
WRFP
Observed (GPCC)
Observed (CRUT)
1997-98
MM5I
CRCM
El Nino
HRM3
Observed (UDEL)
Ensemble
multi-year
drought
3
0
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
small spread, high skill
Correlation with Observed
Precipitation - Coastal California
Model
Correlation
HadRM3
0.857
RegCM3
0.916
MM5
0.925
RSM
0.945
CRCM
0.946
WRF
0.918
Ensemble
0.947
All models have high correlations
with observed monthly time
series of precipitation.
Ensemble mean has a
higher correlation than any
model
Deep South
• Humid mid-latitude climate with substantial
precipitation year around (Koeppen type
Cfa).
• Past studies have found problems
with RCM simulations of
cool-season precipitation in this region.
Monthly Time Series - Deep South
15
mm/day
12
RCM3
CRCM
Observed (GPCC)
Ensemble
MM5I
WRFP
Observed (UDEL)
ECPC
HRM3
Observed (CRUT)
mm/day
9
Correlation
9
HadRM3
0.489
6
RegCM3
0.231
3
MM5
0.343
0
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
RSM
0.649
CRCM
0.649
WRF
0.513
Ensemble
0.640
Monthly mean precipitation for Deep South
12
Model
Ensemble (black curve)
Observed (GPCC)
Observed (UDEL)
Observed (CRUT)
Ensemble
6
3
0
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Two models (RSM and
CRCM) perform much
better. These models
inform the domain interior
about the large scale.
Monthly Time Series - Deep South
15
mm/day
12
RCM3
CRCM
Observed (GPCC)
Ensemble
MM5I
WRFP
Observed (UDEL)
ECPC
HRM3
Observed (CRUT)
mm/day
9
Correlation
9
HadRM3
0.489
6
RegCM3
0.231
3
MM5
0.343
0
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
RSM
0.649
CRCM
0.649
WRF
0.513
Ensemble
0.640
RSM+CRCM
0.727
Monthly mean precipitation for Deep South
12
Model
Ensemble (black curve)
Observed (GPCC)
Observed (UDEL)
Observed (CRUT)
Ensemble
6
3
0
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
A “mini ensemble” of RSM
and CRCM performs best
in this region.
NARCCAP PLAN – Phase II
A2 Emissions Scenario
GFDL
Time slice
50 km
GFDL
1971-2000 current
CGCM3
HADCM3
Provide boundary conditions
CCSM
CAM3
Time slice
50km
2041-2070 future
MM5
RegCM3
CRCM
HADRM3
RSM
WRF
Iowa State/
PNNL
UC Santa Cruz
ICTP
Quebec,
Ouranos
Hadley Centre
Scripps
NCAR/
PNNL
GCM-RCM Matrix
AOGCMS
GFDL
RCMs
MM5
RegCM
CRCM
HADRM
RSM
WRF
*CAM3
*GFDL
X1**
CGCM3
HADCM3
CCSM
X
X1
X**
X1**
X
X
X1**
X1
X
X
X1
X
X**
1 = chosen first GCM
*= time slice experiments
Red = run completed
** = data loaded
Phase II (Climate Change)
Results
Change in Winter Temperature
UK Models
Change in Summer Temperature
UK Models
Uncertainty across two RCMs nested in same
GCM for % Change in Winter Precipitation
Regional Model 1
Global Model
Regional Model 2
Effect of two different GCMs driving one RCM –
% change in winter precipitation
GCMs
CGCM3
RCM
RegCM3
GFDL
Global Time Slice / RCM Comparison
at same resolution (50km)
A2 Emissions Scenario
GFDL
NCAR
CCSM
AOGCM
Six RCMS
50 km
GFDL
AGCM
Time slice
50 km
compare
compare
CAM3
Time slice
50km
RegCM3 in GFDL
% Change Precip - Winter
Quantification of Uncertainty
• The four GCM simulations already ‘situated’
probabilistically based on earlier work (Tebaldi et
al., 2004, 2005)
• RCM results nested in particular GCM would be
represented by a probabilistic model (derived
assuming probabilistic context of GCM simulation)
• Use of performance metrics to differentially weight
the various model results – will use different
metrics – including process level expert judgment
- determine sensitivity of final pdfs to various
methods
NARCCAP Project Timeline
Phase IIa
Current
Climate1
Future Current and
Climate 1
Future 2
Project Start
Phase 1
1/06
9/07
AOGCM
Boundaries
available
12/07
9/08
Archiving Procedures - Implementation
Phase IIb
Time slices
8/09
6/10
The NARCCAP User Community
Three user groups:
•
Further dynamical or statistical downscaling
•
Regional analysis of NARCCAP results
•
Use results as scenarios for impacts and adaptation
studies
www.narccap.ucar.edu
To sign up as user, go to web site – contact Seth McGinnis
Over 200 hundred users registered
End