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Use of Regional Climate
Models in Impacts
Assessments
L. O. Mearns
Institute for the Study of
Society and the Environment
National Center for Atmospheric Research
Colloquium on Climate and Health
Boulder, Colorado
July 17, 2006
Elevation (meters)
2500
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-250
NCAR CSM Topography
2.8 deg. by 2.8 deg.
RegCM Topography
0.5 deg. by 0.5 deg.
Elevation (meters)
3000
2750
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2250
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1750
1500
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1000
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250
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Resolutions Used in Climate Models
• High resolution global coupled oceanatmosphere model simulations are not yet
feasible (~ 250 - 300 km)
• High resolution global atmospheric model
simulations are feasible for time-slice
experiments ~ 50-100 km resolution for 1030 years (~ 100 km)
• Regional model simulations at resolution
10-30 km are feasible for simulations 20-50
years (~ 50 km)
Benefits of High Resolution Modeling
• Improves weather forecasts (e.g., Kalnay et al.
1998), down to to 10 km and improves seasonal
climate forecasts, but more work is needed
(Mitchell et al., Leung et al., 2002).
• Improves climate simulations of large scale
conditions and provides greater regional detail
potentially useful for climate change impact
assessments
• Often improves simulation of extreme events such
as precipitation and extreme phenomena
(hurricanes).
Regional Climate Modeling
• Adapted from mesoscale research or weather
forecast models. Boundary conditions are provided
by large scale analyses or GCMs.
• At higher spatial resolutions, RCMs capture climate
features related to regional forcings such as
orography, lakes, complex coastlines, and
heterogeneous land use.
• GCMs at 200 – 250 km resolution provide
reasonable large scale conditions for downscaling.
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 response
(10s kms)
Now that we can have more regional
detail, what difference does it make in
any given impacts assessment?
What is the added value?
Do we have more confidence in the more
detailed results?
How important is spatial scale versus
other factors regarding simulating future
climate?
Use of Regional Climate Model Results for Impacts
Assessments
• Agriculture:
Brown et al., 2000 (Great Plains – U.S.)
Guereña et al., 2001 (Spain)
Mearns et al., 1998, 1999, 2000, 2001, 2003, 2004
(Great Plains, Southeast, and continental US)
Carbone et al., 2003 (Southeast US)
Doherty et al., 2003 (Southeast US)
Tsvetsinskaya et al., 2003 (Southeast U.S.)
Easterling et al., 2001, 2003 (Great Plains, Southeast)
Thomson et al., 2001 (U.S. Pacific Northwest)
White et al., 2006 (California (wine))
Use of RCM Results for Impacts Assessments 2
•
Water Resources:
Leung and Wigmosta, 1999 (US Pacific Northwest)
Stone et al., 2001, 2003 (Missouri River Basin)
Arnell et al., 2003 (Southern Africa)
Miller et al., 2003 (California)
Payne et al., 2004 (Columbia River Basin)
Wood et al., 2004 (Pacific Northwest)
•
Forest Fires:
Wotton et al., 1998 (Canada – Boreal Forest)
• Human Health:
Hogrefe et al., 2004 (New York City)
Examples of RCM Use in
Climate and Impacts Studies
• Water Resources – Pacific Northwest
• Agriculture – Wine Production in US
• Human Health – New York
• European Prudence Program
• New Program – NARCCAP
ACPI Climate Change Studies
• One control and 3
ensemble future PCM
simulations were used
to drive the RCM for
current and 2040-2060
• Goal: Examine the
effects of climate
change on water
resources in the western
US
Leung et al., 2004
Global and Regional Simulations of
Snowpack
GCM under-predicted and misplaced snow
Regional Simulation
Global Simulation
Climate Change Signals
RCM
PCM
Temperature
Precipitation
Effects of Climate Change on Water
Resources of the Columbia River Basin
• Change in snow water equivalent:
– PCM: - 16%
– RCM: - 32%
• Change in average annual runoff:
– PCM: 0%
– RCM: - 10%
Payne et al., 2004
Changes in Extremes – A2 scenario
RegCM3 nested in FV-GCM
Changes in T95 event frequency (days per year)
and T95 mean heat wave length (days per event)
Diffenbaugh et al., 2005
Climate Change and Wine
Production in the US
Extreme heat could, by the end of the 21st century,
result in loss of 80 percent of wine growing area in the
US.
Significant shift in wine growing areas, to the
Northwest and Northeast. Current wine growing areas
in California, for example, would no longer be viable
areas for wine production.
White et al., 2006
Modeling the Impact of Global Climate and
Regional Land Use Change on Regional
Climate and Air Quality over the Northeastern
United States
C. Hogrefe, J.-Y. Ku, K. Civerolo, J. Biswas, B. Lynn,
D. Werth, R. Avissar, C. Rosenzweig, R. Goldberg, C.
Small, W.D. Solecki, S. Gaffin, T. Holloway, J.
Rosenthal, K. Knowlton, and P.L. Kinney
Hogrefe et al., 2004
U.S. EPA STAR Program
NY Climate & Health Project:
Project Components
•
•
•
•
•
Model Global Climate
Model and Evaluate Land Use
Model Regional Climate
Model Regional Air Pollution (ozone, PM2.5)
Evaluate Health Impacts (heat, air pollution)
– For 2020s, 2050s, and 2080s
IPCC A2, B2 Scenarios
Global Climate Model
NASA-GISS
meteorological variables
Regional Climate
reflectance;
stomatal resistance;
surface roughness
Land Use / Land Cover
SLEUTH,
Remote Sensing
ClimRAMS
MM5
meteorological
variables:
temp., humidity,
etc.
heat
Public Health
Risk Assessment
Ozone
PM2.5
IPCC A2, B2 Scenarios
Air Quality
MODELS-3
Model Setup
• GISS coupled global ocean/atmosphere model driven by
IPCC greenhouse gas scenarios (“A2” high CO2 scenario
presented here)
• MM5 regional climate model takes initial and boundary
conditions from GISS GCM
• MM5 is run on 2 nested domains of 108km and 36km over
the U.S.
• CMAQ is run at 36km to simulate ozone
• 1996 U.S. Emissions processed by SMOKE and – for
some simulations - scaled by IPCC scenarios
• Simulations periods :
June – August 1993-1997
June – August 2053-2057
Daily Maximum O3 Predictions July 9 - 14, 1996
Tests with 12 and 4 km Resolution
Changes in Ozone with
Climate Change
Current
2020
(ppb)
2050
2080
Hogrefe et al. 2004
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 GCMs)
Programs Exploring Multiple
Uncertainties
• PRUDENCE - over Europe
• NARCCAP – over North America
• CREAS: Cenários REgionais de Mudança de
Clima para América do Sul
(Regional Climate Change Scenarios for South
America)
PRUDENCE
Project
Multiple AOGCMs and RCMs over
Europe: Simulations of Future
Climate
Christensen et al., 2006
Summary of RegCM3
Results for A2 and B2 scenarios
Nested in HADAM3 time-slice
• RegCM3 – 50 km
• HadAM3 time slice –
100 km
• Years – 1961-1990 vs.
2070 –2099
• A2 and B2 SRES
scenarios
Giorgi et al., 2004
Summer Temperature Change: B2 & A2 Scenarios
JJA HadAMH: B2
WARM
JJA HadAMH: A2
HOT
JJA RegCM: B2
WARM
JJA RegCM: A2
WARM
NARCCAP
North American Regional
Climate Change Assessment
Program
Multiple AOGCM and RCM Climate
Scenarios Project over North America
www.narccap.ucar.edu
Participants
Linda O. Mearns, National Center for
Atmosheric Research,
Ray Arritt, Iowa State, Daniel Caya,
OURANOS, Phil Duffy, LLNL, Filippo
Giorgi, Abdus Salam ICTP, William
Gutowski, Iowa State, Isaac Held, GFDL,
Richard Jones, Hadley Centre, Rene
Laprise, UQAM, Ruby Leung, PNNL,
Doug Nychka, NCAR Jeremy Pal, ICTP,
John Roads, Scripps, Lisa Sloan, UC
Santa Cruz, Ron Stouffer, GFDL, Gene
Takle, Iowa State, Bill Collins, NCAR,
Francis Zwiers, CCCma
Main NARCCAP Goals
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 models
NARCCAP domain
NARCCAP PLAN
A2 Emissions Scenario
HADAM3
GFDL
CCSM
1960-1990 current
Provide boundary conditions
MM5
Iowa State/
PNNL
link to European
Prudence
CGCM3
2040-2070 future
RegCM3
CRCM
HADRM3
RSM
WRF
UC Santa Cruz
ICTP
Quebec,
Ouranos
Hadley Centre
Scripps
NCAR/
PNNL
Global Time Slice / RCM Comparison
at same resolution (50km)
A2 Emissions Scenario
GFDL
CCSM
AOGCM
Six RCMS
50 km
GFDL
Time slice
50 km
compare
compare
CAM3
Time slice
50km
Final Thoughts
• Exploration of multiple uncertainties
• Establishing greater confidence in high
resolution simulations