1. 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

2. RegCM Topography 0.5 deg. by 0.5 deg. NCAR CSM Topography
Elevation (meters)
2500
2250
2000
1750
1500
1250
1000
750
RegCM Topography
0.5 deg. by 0.5 deg.
500
250
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

3. Resolutions Used in Climate Models
High resolution global coupled ocean-atmosphere model simulations are not yet feasible (~ km)
High resolution global atmospheric model simulations are feasible for time-slice experiments ~ km resolution for years (~ 100 km)
Regional model simulations at resolution km are feasible for simulations years (~ 50 km)

4. 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).

5. 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.

6. 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)

7. Do we have more confidence in the more detailed results?
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?
We still need more studies to more fully determine the above – what difference does it make to use
High resolution scenarios?

8. 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., (Southeast US)
Doherty et al., (Southeast US)
Tsvetsinskaya et al., 2003 (Southeast U.S.)
Easterling et al., 2001, (Great Plains, Southeast)
Thomson et al., 2001 (U.S. Pacific Northwest)
White et al., (California (wine))
This list not exhaustive, but it does show most of the studies that have used regional model results as
Scenarios for impacts - stuides with asterisks indicate those studies that have used the RegCM for scenario development

9. Use of RCM Results for Impacts Assessments 2
Water Resources:
Leung and Wigmosta, 1999 (US Pacific Northwest)
Stone et al., 2001, (Missouri River Basin)
Arnell et al., (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., (New York City)
Most in agriculture and im water resources.

10. 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

11. ACPI Climate Change Studies
One control and 3 ensemble future PCM simulations were used to drive the RCM for current and
Goal: Examine the effects of climate change on water resources in the western US
Two river basins in red – northern one is the Columbia River Basin
Leung et al., 2004

12. Global and Regional Simulations of Snowpack GCM under-predicted and misplaced snow
Global Simulation

13. Climate Change Signals
Temperature
Precipitation
PCM
RCM

14. Effects of Climate Change on Water Resources of the Columbia River Basin
Change in snow water equivalent:
PCM: %
RCM: %
Change in average annual runoff:
PCM: 0%
RCM: - 10%
Payne et al., 2004

15. Changes in Extremes – A2 scenario RegCM3 nested in FV-GCM
Reference integration and Climate change A2 scenario using boundary conditions from
NASA finite volume GCM (FV-GCM) 2071— Resolution of RegCM3 = 25km
Changes in T95 event frequency (days per year) and T95 mean heat wave length (days per event)
Diffenbaugh et al., 2005

16. 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

17. 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

18. 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
Now that I’ve finished with the project overview, I’m going to tell you about my role in it…
Evaluating Health Impacts, HEAT from GCM output = Phase I;
AQ later, when other groups have completed their work

19. NASA-GISS ClimRAMS MM5 Risk Assessment SLEUTH, Remote Sensing MODELS-3
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
Land Use / Land Cover
SLEUTH,
Remote Sensing
meteorological
variables:
temp., humidity, etc.
PAUSE…..
Each bubble represents a whole research community & a very active body of work, but that typically are VERY SEPARATE from one another
The UNIQUE contribution of this project is that we’re LINKING them together for the first time, and applying state-of-the-art in each case to project how HEALTH may be affected by climate change
NONE of the individual groups alone could answer the central research question
[Get SPECIFIC about what each of the model links]
1) GCM: “Some of you may not know that, above Tom’s Restaurant on Seinfeld, is a NASA lab”…
We work with the GCM group there because their models are widely used & tested, have reasonable results, & they’re local.
GCMs use storylines or “scenarios” of possible future global changes (negotiated by the IPCC): A2=HI GROWTH, B2=LO GROWTH
But the scale they work at is HUGE: model grid resolution 4 x 5 degrees lat/long, 400x500km or 250x300 miles (half of Pennsyl); TOO BIG to tell a meaningful story about NY PH…
2) …that’s why we’re downscaling to the RCMs & AQMs, which run at km (20-60 miles);
But that’s STILL too big for our project, to tell the local PH story (Manhattan is 16 km, 10 mi long)
3) AQM: takes winds, temp, RH from RCM + emission inputs from EPA models;
this project will change emissions projections, to reflect scenarios of future growth, as defined by the IPCCs A2/B2
(same as GCM) & LU uses;
4) LU: extrapolates current trends in development (“sprawl”) into future
PH-driven exciting project, but still we can’t go finer scale due to computing time & storage constraints; working to overcome scale & computing LIMITATIONS to this project in future [but it’s important to critically evaluate the model
RECAP: A2/B2 drives GCM; GCM to RCM; RCM to PH (heat)
LU to RCM, RCM to AQ, AQ to PH (PM2.5 & O3)
Ozone
PM2.5
Air Quality
MODELS-3
IPCC A2, B2 Scenarios

20. 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
June – August

22. Daily Maximum O3 Predictions July 9 - 14, 1996

23. Tests with 12 and 4 km Resolution

24. Changes in Ozone with Climate Change
Current
(ppb)
2020
Changes in ozone concentration with climte change future times sampled – 2020’s, 2050’s, 2080s
2080
2050
Hogrefe et al. 2004

25. 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)

26. 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)

27. Multiple AOGCMs and RCMs over Europe: Simulations of Future Climate
PRUDENCE Project
Multiple AOGCMs and RCMs over Europe: Simulations of Future Climate
Christensen et al., 2006

28. Summary of RegCM3 Results for A2 and B2 scenarios Nested in HADAM3 time-slice
RegCM3 – 50 km
HadAM3 time slice –
100 km
Years – vs –2099
A2 and B2 SRES scenarios
Giorgi et al., 2004

29. Summer Temperature Change: B2 & A2 Scenarios
JJA HadAMH: B2
JJA RegCM: B2
WARM
WARM
JJA HadAMH: A2
JJA RegCM: A2
WARM
HOT

30. NARCCAP North American Regional Climate Change Assessment Program
Multiple AOGCM and RCM Climate Scenarios Project over North America

31. Linda O. Mearns, National Center for Atmosheric Research,
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

32. 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

33. NARCCAP domain

34. NARCCAP PLAN A2 Emissions Scenario HADAM3 GFDL CCSM CGCM3 RegCM3 CRCM
link to European
Prudence
GFDL
CCSM
CGCM3
current
Provide boundary conditions
future
RegCM3
UC Santa Cruz
ICTP
CRCM
Quebec,
Ouranos
HADRM3
Hadley Centre
RSM
Scripps
WRF
NCAR/
PNNL
MM5
Iowa State/
PNNL

35. 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

36. Final Thoughts Exploration of multiple uncertainties
Establishing greater confidence in high resolution simulations