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 800000 people
• May add 300000 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
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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.
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
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
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
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