Download Regional Modeling. - Advanced Study Program

Use of Regional Models in
Impacts Assessments
L. O. Mearns
NCAR
Colloquium on Climate and Health
NCAR, Boulder, CO
July 22, 2004
“Most GCMs neither incorporate nor provide information on
scales smaller than a few hundred kilometers. The effective
size or scale of the ecosystem on which climatic impacts
actually occur is usually much smaller than this. We are
therefore faced with the problem of estimating climate
changes on a local scale from the essentially large-scale
results of a GCM.”
Gates (1985)
“One major problem faced in applying GCM projections to
regional impact assessments is the coarse spatial scale of
the estimates.”
Carter et al. (1994)
But, once we 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?
Elevation (meters)
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0
-250
NCAR CSM Topography
2.8 deg. by 2.8 deg.
RegCM Topography
0.5 deg. by 0.5 deg.
Elevation (meters)
3000
2750
2500
2250
2000
1750
1500
1250
1000
750
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250
0
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)
RCM Nesting Technique
Regional Climate Model Schematic
GLCC
Vegetation
Reanalysis
& GCM
Initial and
Boundary
Conditions
Hadley & OI
Sea Surface
Temperatures
USGS
Topography
Rotated
Mercator
Projection
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)
Pona et al., (in Mearns, 2001) (Italy)
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 (South Africa)
Miller et al., 2003 (California)
Wood et al., 2004 (Pacific Northwest)
•
Forest Fires:
Wotton et al., 1998 (Canada – Boreal Forest)
• Human Health:
New York City Health Project (ongoing)
Examples of RCM Use in
Climate and Impacts Studies
• Precipitation and Hydrology over S.
Africa
• Water Resources in Pacific Northwest
• Agriculture - Southeast US
• Human Health – New York
• European Prudence Program
• New Program – NARCCAP
Regional Climate Modeling and
Hydrological Impacts in Southern
Africa
Arnell et al., 2003,
J. Geophys. Research
Arnell et al., 2003, J. of Geophys. Res.
Arnell et al., 2003, J. of Geophys. Res.
Climate Simulations of Western U.S.
Strong Effect of Terrain
Model ability to resolve terrain features is critical
Observed snow pack , March, 1998
Leung et al., 2004,
Climatic Change (Jan.)
Observed mean annual precipitation
Elevation (meters)
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2250
2000
1750
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1000
750
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250
0
-250
MM5 Topography
0.5 deg. by 0.5 deg.
Elevation (meters)
3000
2750
NCAR/DOE Topography
2.8 deg. by 2.8 deg.
2500
2250
2000
1750
1500
1250
1000
750
500
250
0
Observed and Simulated El Nino
Precipitation Anomaly
RCM reproduces mesoscale features associated
with ENSO events
Observation
RCM Simulation
NCEP Reanalyses
Global and Regional Simulations of
Snowpack
GCM under-predicted and misplaced snow
Regional Simulation
Global Simulation
Climate Change Signals
RCM
PCM
Temperature
Precipitation
Extreme Precipitation/Snowpack Changes
Lead to significant changes in streamflow affecting hydropower
production, irrigation, flood control, and fish protection
Special Issue of Climatic Change (60:1-148)
Issues in the Impacts of Climatic Variability and
Change on Agriculture
1.
Mearns, L. O., Introduction to the Special Issue on the Impacts of Climatic
Variability and Change on Agriculture
2.
Mearns, L. O., F. Giorgi, C. Shields, and L. McDaniel, Climate Scenarios for
the Southeast US based on GCM and Regional Model Simulations.
3.
Tsvetsinskaya, E., L. O. Mearns, T. Mavromatis, W. Gao, L. McDaniel, and M.
Downton,The Effect of Spatial Scale of Climate Change Scenarios on
Simulated Maize, Wheat, and Rice Production in the Southeastern United
States.
4.
Carbone, G., W. Kiechle, C. Locke, L. O. Mearns, and L. McDaniel, Response
of Soybeans and Sorghum to Varying Spatial Scales of Climate Change
Scenarios in the Southeastern United States.
5.
Doherty, R. M., L. O. Mearns, R. J. Reddy, M. Downton, and L. McDaniel, A
Sensitivity Study of the Impacts of Climate Change at Differing Spatial Scales
on Cotton Production in the SE USA.
6.
Adams, R. M., B. A. McCarl, and L. O. Mearns, The Economic Effects of
Spatial Scale of Climate Scenarios: An Example From U. S. Agriculture.
Models Employed
• Commonwealth Scientific and Industrial Research
Organization (CSIRO) GCM – Mark 2 version
•
•
•
•
•
Spectral general circulation model
Rhomboidal 21 truncation (3.2 x 5.6); 9 vertical levels
Coupled to mixed layer ocean (50 m)
30 years control and doubled CO runs
NCAR RegCM2
•
•
•
•
50 km grid point spacing, 14 vertical levels
Domain covering southeastern U.S.
5 year control run
5 year doubled CO runs
Domain of RegCM
denotes study area
+ denotes RegCM Grid Point (~ 0.5o)
X denotes CSIRO Grid Point (3.2 o lat. 5.6 o long)
RegCM Topography (meters)
Contour from 100 to 4000 by 100 (x1)
Climate Change - Δ Temperature (oC)
CSIRO
RegCM
CSIRO
RegCM
Summer
Fall
Minimum Temperature
7.00
to
10.00
6.00
to
7.00
5.00
to
6.00
4.00
to
5.00
Maximum Temperature
3.00
to
4.00
2.00
to
3.00
1.00
to
2.00
0.00
to
1.00
-1.00
to
0.00
% Change in Corn Yields
Conclusions
Regionalization of the climate change
scenarios matters in terms of the economic
indicators of the ASM
• Shows up in aggregate economic welfare
(different orders of magnitude);
• Regional patterns of agricultural production
are altered;
- more spatial variability with RegCM;
- Southern states are more negatively
affected by RegCM.
Conclusions (Con’t.)
• The contrast in economic net welfare based
on spatial scale of climate scenarios is
similar in magnitude to the economic
contrast resulting from use of two very
different AOGCM simulations in the US
National Assessment.
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
This project is supported by the U.S. Environmental
Projection Agency under STAR grant R-82873301
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
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
A Model Look Into the 2050’s
• How will modeled temperature and ozone
in the northeastern U.S. change under the
“A2” (high CO2 growth) scenario (assume
constant VOC and NOx emissions)?
• How will CMAQ ozone predictions change
when IPCC “A2” projected changes in
ozone precursor emissions (VOC+8%,
NOx+29.5%) are included in the
simulation?
Research questions
• Health Risk Assessment:
– Deaths due to short-term heat exposures
– Hospital admissions due to short-term ozone exposures
• Development of model linkages
• Scale Intercomparison: as we go from 108 -> 36
-> 4km scale, what difference do we see in impact
estimates? How do model results compare at
different scales for NY metro region.
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
Daily Maximum O3 Predictions July 9 - 14, 1996
MM5 Current Climate
GCM and RCM
Projections
Tests with 12 and 4 km Resolution
RCMs and Simulation of
Extremes
Do they do better?
1993 Midwest
Summer Flood
• Record high rainfall (>200
year event)
USHCN Observations
• Thousands homeless
• 48 deaths
• $15-20 billion in Damage
J. Pal
RegCM
1988 Great North
American Drought
• Driest/warmest since 1936
• $30 billion in Agricultural
Damage
CRU Observations
RegCM
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)
PRUDENCE
Project
Multiple AOGCMs and RCMs over
Europe: Simulations of Future
Climate
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
• Control run results
• Changes in Climate
Giorgi et al., 2004
Emissions Scenarios
CO2 Emissions
(Gt C)
CO2 Concentrations
(ppm)
A2
A2
B2
B2
Map of Domain & Topography
Winter Precipitation: Reference Simulation
DJF CRU
DJF HadAMH
DJF RegCM
Summer Surface Air Temperature: Reference Simulation
JJA CRU
JJA HadAMH
JJA RegCM
Summer Precipitation: Reference Simulation
JJA CRU
JJA HadAMH
JJA RegCM
Winter Temperature Change: B2 & A2 Scenarios
DJF HadAMH: B2
WARM
DJF HadAMH: A2
HOT
DJF RegCM: B2
WARM
DJF RegCM: A2
WARM
Summer Temperature Change: B2 & A2 Scenarios
JJA HadAMH: B2
WARM
JJA HadAMH: A2
HOT
JJA RegCM: B2
WARM
JJA RegCM: A2
WARM
Summer Precipitation Change: B2 & A2 Scenarios
JJA HadAMH: B2
JJA RegCM: B2
WET
DRY
WET
DRY
JJA HadAMH: A2
JJA RegCM: A2
WET
DRY
WET
DRY
NARCCAP
North American Regional
Climate Change Assessment
Program
Multiple AOGCM and RCM Climate
Scenarios Project over North America
Participants
Linda O. Mearns, National Center for
Atmosheric Research,
Ray Arritt, Iowa State, George Boer,
CCCma, 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, Jeremy Pal, ICTP, John
Roads, Scripps, Lisa Sloan, UC Santa
Cruz, Ron Stouffer, GFDL, Gene Takle,
Iowa State, Warren Washington, 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
When to Use High Resolution
• Consider the importance of regional detail
compared to other uncertainties in project
• High resolution useful when there are high
resolution forcings: complex topography,
complex coastlines, islands, heterogeneous landuse
• Consider also statistical downscaling (Wilby)
• More guidance on web at:
www. ipcc-ddc.cru.uea.ac.uk