The Importance/Unimportance of High Resolution Information on Future Regional Climate for Coping with Climate Change

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The Importance/Unimportance of High Resolution
Information on Future Regional Climate for Coping
with Climate Change Linda O. Mearns National
Center for Atmospheric Research
PCC/CIG University of Washington Seattle,
Washington October 27, 2009
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Global forecast models
Climate Models
Regional models
Global models in 5 yrs
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The Mismatch of Scale Issue
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) downscaling techniques are commonly
used to address the scale mismatch between coarse
resolution GCMs and the local catchment scales
required for hydrologic modeling Fowler and
Wilby (2007)
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But, once we have more regional detail, what
difference does it make in any given
impacts/adaptation assessment? What is the added
value? Do we have more confidence in the more
detailed results?
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Different Kinds of Downscaling

• Simple (Giorgi and Mearns, 1991)
• adding large scale climate changes to higher
  resolution observations (the delta approach)
• More sophisticated - interpolation of coarser
  resolution results (Maurer et al. 2002, 2007)
• Statistical
• Statistically relating large scale climate
  features (e.g., 500 mb heights) to local climate
  (e.g, daily, monthly temperature at a point)
• Dynamical
• application of regional climate model using
  AOGCM boundary conditions
• Confusions when the term downscaling is used
  could mean any of the above

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Examples of Resolutions Used in Recent Climate
Impacts Studies

• Ecology
• Water Resources
• Heat Stress

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Ecology Example

• Projected climate-induced faunal change in the
  Western Hemisphere. Lawler et al. 2009, Ecology
• Used 10 AOGCMs, 3 emissions scenarios,
  essentially interpolated to 50 km scale
• Applied to bioclimatic models (associates current
  range of species to current climate)

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Sample Results
Predictions of climate-induced species turnover
for three emissions scenarios (GB1, HA1B,
IA2) for 2071-2100.
Conclusion projected severe faunal change
even lowest scenarios indicates substantial
change in biodiversity
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What is the value of information on future
climate to water resource managers?

• Climate change and water management in the Chino
  Basin, CA
• Characterizations of uncertainty used in
  workshops
• Traditional scenarios without probabilities
• Probability-weighted scenarios
• Scenarios constructed through robust decision
  making methods

Groves and Lempert, 2006
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Inland Empire Utilities Agency (IEUA), based in
Chino, CA Faces Significant Water Challenges

• IEUA currently serves 800,000 people
• May add 300,000 by 2025

• Current water sources include
• Groundwater 56
• Imports 32
• Recycled 1
• Surface 8
• Desalter 2

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ResultsGroves and Lempert, 2006

• Climate information from CMIP3 downscaled to 12
  km (Maurer et al. 2002, 2007)
• Traditional scenarios appear to give participants
  much of the information they needed
• Emphasized importance of achieving goals of 20
  Year Plan to address climate change in addition
  to population growth
• But this was their first exposure to climate
  change information
• Probabilities raised potential of low likelihood,
  extremely large shortages
• IEUA has significant adaptive capacity to
  address historic natural variability of
  California climate
• Probabilistic information quickly prompted
  discussion of strengths and limits of adaptive
  capacity

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Heat Stress Study Regional Relationships
Income, Vegetation, and Temperature
Neighborhood Income Distribution

• Hypothesis The distribution of urban
  vegetation is an important intermediary between
  patterns of human settlement and local
  temperature.

Harlan et al., Arizona State Urban Heat Study
(under way)
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WRF Model - Multiple nesting

• Simulations
• Hourly air temperature, humidity, wind speed for
• Past typical summer for model validation.
• At least one future wet and dry summer.

Computational Effort 1 day simulation needs 3
CPU hours on IBM supercomputer Simulations for
180 days per summer 22.5 days
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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)
• Olesen et al., 2007 (Europe)

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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
• Hogrefe et al., 2004

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Do we need dynamical or statistical DS for
formulating actual regional or local adaptation
plans?

• Many statements in literature claim yes
• But there are many other uncertainties associated
  with regional climate change (e.g., missing
  processes in models, mis-specified processes,
  different responses of AOGCMs)
• Danger of false realism people recognize
  their region and may become too anchored to the
  detail to the exclusion of other uncertainties
• Do we need to focus more on another part of the
  problem i.e., managing the uncertainty for
  decision making rather than trying to create
  greater precision in future climate?

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Use of Climate Informationin Adaptation Planning
Location Emissions Climate Models Downscaling Used Notes Reference
Gulf Coast 3 SRES AR4 Multiple None SAP 4.7
California 2 SRES 2 GCMs Simple Cayan et al. 2008
Maryland 2 SRES 17 AR4 Simple Boesch et al. 2008
Colorado River 2 SRES 19 AR4 None Seager et al. 2007
New York City 3 SRES A2, A1B, B1 Multiple ranges of changes in key variables Simple Sea level rise scenarios - mod of IPCC 2007 NPCC, 2009
King County 2 SRES A2, B1 10 AR4 Simple Mote et al., 2005
Miami Dade County None None None Sea level rise scenarios ??
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NYC Adaptation Plan

• Climate change information taken from global
  climate models ranges given for different
  decades (e.g., 1.5 3F increase and 0 5
  increase in precipitation, sea level rise of 2 5
  inches by the 2020s).
• Delta method applied to higher res observations
• Adaptation plans have been made using this type
  of climate change information
• Would higher resolution information have
  substantially altered these plans?

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What high res is really good for

• Can act as go-between between bottom-up and
  top-down approaches to IAV research (e.g., urban
  heat wave studies)
• For coupling climate models to other models that
  require high resolution (e.g. air quality models
  for air pollution studies)
• In certain specific contexts, provides insights
  on realistic climate response to high resolution
  forcing (e.g. mountains)

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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
Leung et al., 2004
PCM
PCM GCM
RCM (MM5) nested in PCM
RCM
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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
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WINTER PRECIPITATION OVER GREAT BRITAIN
300km Global Model
50km Regional Model
25km Regional Model
Observed
(HC models)?
R. Jones UKMO
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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 AOGCMs)

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Oleson et al., 2007, Suitability for Maize
cultivation

• Based on PRUDENCE Experiments over Europe
• Uncertainties in projected impacts of climate
  change on European agriculture and terrestrial
  ecosystems based on scenarios from regional
  climate models

a. 7 RCMs,
one Global model, one emissions scenario
b. 24 scenarios from
6 GCMs, 4 emission scenarios Conclusion
Uncertainty across GCMs (considering large number
of GCMs) larger than across RCMs, BUT uncertainty
from RCMs larger than uncertainty from only GCMs
used in PRUDENCE
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Mother Of All Ensembles
The Future
scenario
scenario
scenario
GCM ensemble member
RCM
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CORDEX domains
ENSEMBLES
NARCCAP
RCMIP
CLARIS
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CORDEX Phase I experiment design
Model Evaluation Framework
Climate Projection Framework
Multiple regions (Initial focus on Africa) 50 km
grid spacing
ERA-Interim BC 1989-2007
RCP4.5, RCP8.5
Multiple AOGCMs
Regional Analysis Regional Databanks
1951-2100 1981-2010, 2041-2070, 2011-2040
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UKCP02 and 09 Scenarios (50 km, 25 km)

• Stakeholders do request high res climate
  scenarios but one can question the actual
  suitability for user needs, as well as
  credibility and legitimacy of high res scenarios
  since higher resolution (in the UK case) was
  achieved at the expense of more comprehensive
  assessment of climate uncertainty (Hulme and
  Desai, 2008).
• Programs are scenario driven rather than decision
  driven

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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
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NARCCAP - Team

• Linda O. Mearns, NCAR
• Ray Arritt, Iowa State, Dave Bader, LLNL, Wilfran
  Moufouma-Okia, Hadley Centre, Sébastien Biner,
  Daniel Caya, OURANOS, Phil Duffy, LLNL and
  Climate Central, Dave Flory, Iowa State, Filippo
  Giorgi, Abdus Salam ICTP, William Gutowski, Iowa
  State, Isaac Held, GFDL, Richard Jones, Hadley
  Centre, Bill Kuo, NCAR René Laprise, UQAM, Ruby
  Leung, 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, Ron
  Stouffer, GFDL, Gene Takle, Iowa State, Tom
  Wigley, NCAR

Deceased June 2008
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NARCCAP Domain
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Organization of Program

• Phase I 25-year simulations using
  NCEP-Reanalysis boundary conditions (19792004)
• 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).

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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 five
  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

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Regions Analyzed
Boreal forest
Maritimes
Great Lakes
Pacific coast
Upper Mississippi River
Deep South
California coast
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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
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Monthly time series of precipitation in coastal
California
small spread, high skill
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Correlation with Observed Precipitation - Coastal
California
All models have high correlations with observed
monthly time series of precipitation.
Model Correlation
HadRM3 0.857
RegCM3 0.916
MM5 0.925
RSM 0.945
CRCM 0.946
WRF 0.918
Ensemble 0.947
Ensemble mean has a higher correlation than any
model
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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.

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Monthly Time Series - Deep South
Model Correlation
HadRM3 0.489
RegCM3 0.231
MM5 0.343
RSM 0.649
CRCM 0.649
WRF 0.513
Ensemble 0.640
Ensemble (black curve)
Two models (RSM and CRCM) perform much better.
These models inform the domain interior about the
large scale.
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Monthly Time Series - Deep South
Model Correlation
HadRM3 0.489
RegCM3 0.231
MM5 0.343
RSM 0.649
CRCM 0.649
WRF 0.513
Ensemble 0.640
RSMCRCM 0.727
Ensemble (black curve)
A mini ensemble of RSM and CRCM performs best
in this region.
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NARCCAP PLAN Phase II
A2 Emissions Scenario
GFDL
CCSM
HADCM3
CGCM3
CAM3 Time slice 50km
GFDL Time slice 50 km
1971-2000 current
2041-2070 future
Provide boundary conditions
CRCM Quebec, Ouranos
RegCM3 UC Santa Cruz ICTP
HADRM3 Hadley Centre
MM5 Iowa State/ PNNL
RSM Scripps
WRF NCAR/ PNNL
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GCM-RCM Matrix
AOGCMS
GFDL CGCM3 HADCM3 CCSM
MM5 X X1
RegCM X1 X
CRCM X1 X
HADRM X X1
RSM X1 X
WRF X X1

CAM3 X
GFDL X


1 chosen first GCM 1 chosen first GCM
time slice experiments Red run completed data loaded time slice experiments Red run completed data loaded time slice experiments Red run completed data loaded
RCMs
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Phase II (Climate Change) Results

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Temperature and precipitation changes with model
agreement (2080-2099 minus 1980-1999) A1B
Scenario
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Change in Winter TemperatureUK Models
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Change in Winter TemperatureCanadian Models

• Global Model

• Regional Model

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Change in Summer TemperatureUK Models
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Change in Summer TemperatureCanadian Models
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Change in Winter PrecipUK Models
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Change in Winter PrecipCanadian Models
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Change in Summer PrecipUK Models
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Change in Summer PrecipCanadian Models
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Summer Temp Changes 2051-20701980-1999
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Global Time Slice / RCM Comparison at same
resolution (50km)
A2 Emissions Scenario
GFDL AOGCM
NCAR CCSM
Six RCMS 50 km
GFDL AGCM Time slice 50 km
CAM3 Time slice 50km
compare
compare
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Future-current Summer Temperatures
GFDL CM2.1
GFDL AM2.1
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RegCM3 in GFDLChange in Summer Temperature
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RegCM3 in GFDLChange in Winter Temperature
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RegCM3 in GFDL Change Precip - Winter
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Quantification of Uncertainty

• The four GCM simulations already situated
  probabilistically based on earlier work (Tebaldi
  et al., 2004)
• 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

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Different Kinds of Downscaling

• Simple (Giorgi and Mearns, 1991)
• Adding coarse scale climate changes to higher
  resolution observations (the delta approach)
• More sophisticated - interpolation of coarser
  resolution results (Maurer et al. 2002, 2007)
• Statistical
• Statistically relating large scale climate
  features (e.g., 500 mb heights), predictors, to
  local climate (e.g, daily, monthly temperature at
  a point), predictands
• Dynamical
• Application of regional climate model using
  global climate model boundary conditions
  several other types stretched grid, etc.
• Confusion can arise when the term downscaling
  is used could mean any of the above

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Probability of temperature change for Colorado,
Spring- A2 scenario
GFDL
HadCM3
CCSM
CGCM
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Probability of temperature change for Colorado,
summer - A2 scenario
GFDL
HadCM3
CCSM
CGCM
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Adaptation Planning for Water Resources

• Develop adaptation plans for Colorado River
  water resources with stakeholders
• Use NARCCAP scenarios, simple DS, statistical
  DS
• Determine value of different types of higher
  resolution scenarios for adaptation plans
• NCAR, Bureau of Reclamation, and Western Water
  Assessment

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NARCCAP Project Timeline
Phase IIa
Current climate1
Future climate 1
Current and Future 2
Project Start
AOGCM Boundaries available
Phase 1
6/09
12/07
9/07
1/06
2/10
9/08
Archiving Procedures - Implementation
Phase IIb
Time slices
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The NARCCAP User Community

• Three user groups
• Further dynamical or statistical downscaling
  
• Regional analysis of NARCCAP results
• Use results as scenarios for impacts studies
  
• www.narccap.ucar.edu
• To sign up as user, go to web site contact Seth
  McGinnis,
• mcginnis_at_ucar.edu

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End