Use of Regional Models in Impacts Assessments

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Use of Regional Models in Impacts Assessments

• L. O. Mearns
• NCAR
• Colloquium on Climate and Health
• NCAR, Boulder, CO
• July 22, 2004

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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)
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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?
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Elevation (meters)
2500
2250
2000
1750
1500
1250
1000
750
RegCM Topography 0.5 deg. by 0.5 deg.
500
250
0
Elevation (meters)
-250
3000
2750
NCAR CSM Topography 2.8 deg. by 2.8 deg.
2500
2250
2000
1750
1500
1250
1000
750
500
250
0
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Resolutions Used in Climate Models

• High resolution global coupled ocean-atmosphere
  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 10-30
  years ( 100 km)
• Regional model simulations at resolution 10-30
  km are feasible for simulations 20-50 years ( 50
  km)

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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).

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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.

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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)

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RCM Nesting Technique
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Regional Climate Model Schematic
Hadley OI Sea Surface Temperatures
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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)

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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)

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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

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Regional Climate Modeling and Hydrological
Impacts in Southern Africa

• Arnell et al., 2003,
• J. Geophys. Research

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Arnell et al., 2003, J. of Geophys. Res.
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Arnell et al., 2003, J. of Geophys. Res.
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Climate Simulations of Western U.S. Strong
Effect of Terrain Model ability to resolve
terrain features is critical
Leung et al., 2004, Climatic Change (Jan.)
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Elevation (meters)
2500
2250
2000
1750
1500
1250
1000
750
MM5 Topography 0.5 deg. by 0.5 deg.
500
250
0
Elevation (meters)
-250
3000
2750
2500
NCAR/DOE Topography 2.8 deg. by 2.8 deg.
2250
2000
1750
1500
1250
1000
750
500
250
0
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Observed and Simulated El Nino Precipitation
AnomalyRCM reproduces mesoscale features
associated with ENSO events
RCM Simulation
Observation
NCEP Reanalyses
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Global and Regional Simulations of SnowpackGCM
under-predicted and misplaced snow
Regional Simulation
Global Simulation
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Climate Change Signals
Temperature
Precipitation
PCM
RCM
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Extreme Precipitation/Snowpack ChangesLead to
significant changes in streamflow affecting
hydropower production, irrigation, flood control,
and fish protection
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Special Issue of Climatic Change (601-148)
Issues in the Impacts of Climatic Variability
and Change on Agriculture

• Mearns, L. O., Introduction to the Special Issue
  on the Impacts of Climatic Variability and
  Change on Agriculture
• Mearns, L. O., F. Giorgi, C. Shields, and L.
  McDaniel, Climate Scenarios for the Southeast US
  based on GCM and Regional Model Simulations.
• 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.
• 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.
• 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.
• 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.

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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

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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)
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RegCM Topography (meters)
Contour from 100 to 4000 by 100 (x1)
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Climate Change - ? Temperature (oC)
CSIRO
RegCM
CSIRO
RegCM
Summer
Fall
Minimum Temperature
Maximum Temperature
5.00 to 6.00
3.00 to 4.00
-1.00 to 0.00
7.00 to 10.00
6.00 to 7.00
2.00 to 3.00
4.00 to 5.00
1.00 to 2.00
0.00 to 1.00
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Change in Corn Yields
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Regionalization of the climate change scenarios
matters in terms of the economic indicators of
the ASM
Conclusions

• 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.

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Conclusions (Cont.)

• 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.

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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
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NY Climate Health ProjectProject 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

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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

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A Model Look Into the 2050s

• 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 (VOC8, NOx29.5) are included in the
  simulation?

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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 -gt 36
  -gt 4km scale, what difference do we see in impact
  estimates? How do model results compare at
  different scales for NY metro region.

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Global Climate Model NASA-GISS
IPCC A2, B2 Scenarios
meteorological variables
Regional Climate ClimRAMS MM5
reflectance stomatal resistance surface
roughness
heat
Public Health Risk Assessment
meteorological variables temp., humidity, etc.
Land Use / Land Cover SLEUTH, Remote Sensing
Ozone PM2.5
Air Quality MODELS-3
IPCC A2, B2 Scenarios
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Daily Maximum O3 Predictions July 9 - 14, 1996
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MM5 Current Climate
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GCM and RCM Projections
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Tests with 12 and 4 km Resolution
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RCMs and Simulation of Extremes

• Do they do better?

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1993 Midwest Summer Flood

• Record high rainfall (gt200 year event)
• Thousands homeless
• 48 deaths
• 15-20 billion in Damage

USHCN Observations
J. Pal
RegCM
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1988 Great North American Drought
CRU Observations

• Driest/warmest since 1936
• 30 billion in Agricultural Damage

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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)

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PRUDENCE Project

• Multiple AOGCMs and RCMs over Europe Simulations
  of Future Climate

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Summary of RegCM3Results for A2 and B2
scenariosNested 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
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Emissions Scenarios
CO2 Emissions (Gt C)
CO2 Concentrations (ppm)
A2
A2
B2
B2
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Map of Domain Topography
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Winter Precipitation Reference Simulation
DJF CRU
DJF RegCM
DJF HadAMH
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Summer Surface Air Temperature Reference
Simulation
JJA CRU
JJA RegCM
JJA HadAMH
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Summer Precipitation Reference Simulation
JJA CRU
JJA RegCM
JJA HadAMH
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Winter Temperature Change B2 A2 Scenarios
DJF HadAMH B2
DJF RegCM B2
WARM
WARM
DJF RegCM A2
DJF HadAMH A2
WARM
HOT
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Summer Temperature Change B2 A2 Scenarios
JJA HadAMH B2
JJA RegCM B2
WARM
WARM
JJA RegCM A2
JJA HadAMH A2
WARM
HOT
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Summer Precipitation Change B2 A2 Scenarios
JJA HadAMH B2
JJA RegCM B2
WET
WET
DRY
DRY
JJA RegCM A2
JJA HadAMH A2
WET
WET
DRY
DRY
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NARCCAP North American Regional Climate Change
Assessment Program

• Multiple AOGCM and RCM Climate Scenarios Project
  over North America

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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

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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
•  

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NARCCAP domain
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NARCCAP PLAN
A2 Emissions Scenario
HADAM3 link to European Prudence
GFDL
CCSM
CGCM3
1960-1990 current
2040-2070 future
Provide boundary conditions
CRCM Quebec, Ouranos
RegCM3 UC Santa Cruz ICTP
HADRM3 Hadley Centre
RSM Scripps
WRF NCAR/ PNNL
MM5 Iowa State/ PNNL
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Global Time Slice / RCM Comparison at same
resolution (50km)
A2 Emissions Scenario
GFDL AOGCM
CCSM
Six RCMS 50 km
CAM3 Time slice 50km
GFDL Time slice 50 km
compare
compare
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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 land-use
• Consider also statistical downscaling (Wilby)
• More guidance on web at
• www. ipcc-ddc.cru.uea.ac.uk