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

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


1
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
2
Global forecast models
Climate Models
Regional models
Global models in 5 yrs
3
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)
4
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?
5
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

6
Examples of Resolutions Used in Recent Climate
Impacts Studies
  • Ecology
  • Water Resources
  • Heat Stress

7
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)

8
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
9
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
10
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

11
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

12
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)
13
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
14
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)

15
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

16
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?

17
Use of Climate Informationin Adaptation Planning
18
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?

19
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)

20
Global and Regional Simulations of SnowpackGCM
under-predicted and misplaced snow
Regional Simulation
Global Simulation
21
Climate Change Signals
Temperature
Precipitation
Leung et al., 2004
PCM
PCM GCM
RCM (MM5) nested in PCM
RCM
22
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
23
WINTER PRECIPITATION OVER GREAT BRITAIN
300km Global Model
50km Regional Model
25km Regional Model
Observed
(HC models)?
R. Jones UKMO
24
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)

25
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
26
Mother Of All Ensembles
The Future
scenario
scenario
scenario
GCM ensemble member
RCM
27
CORDEX domains
ENSEMBLES
NARCCAP
RCMIP
CLARIS
28
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
29
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

30
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
31
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
32
NARCCAP Domain
33
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).

34
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

35
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Regions Analyzed
Boreal forest
Maritimes
Great Lakes
Pacific coast
Upper Mississippi River
Deep South
California coast
40
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
41
Monthly time series of precipitation in coastal
California
small spread, high skill
42
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
43
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.

44
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.
45
Monthly Time Series - Deep South
Ensemble (black curve)
A mini ensemble of RSM and CRCM performs best
in this region.
46
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
47
GCM-RCM Matrix
AOGCMS
RCMs
48
Phase II (Climate Change) Results

49
Temperature and precipitation changes with model
agreement (2080-2099 minus 1980-1999) A1B
Scenario
50
Change in Winter TemperatureUK Models
51
Change in Winter TemperatureCanadian Models
  • Global Model
  • Regional Model

52
Change in Summer TemperatureUK Models
53
Change in Summer TemperatureCanadian Models
54
Change in Winter PrecipUK Models
55
Change in Winter PrecipCanadian Models
56
Change in Summer PrecipUK Models
57
Change in Summer PrecipCanadian Models
58
Summer Temp Changes 2051-20701980-1999
59
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
60
Future-current Summer Temperatures
GFDL CM2.1
GFDL AM2.1
61
RegCM3 in GFDLChange in Summer Temperature
62
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63
RegCM3 in GFDLChange in Winter Temperature
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65
RegCM3 in GFDL Change Precip - Winter
66
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

67
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

68
Probability of temperature change for Colorado,
Spring- A2 scenario
GFDL
HadCM3
CCSM
CGCM
69
Probability of temperature change for Colorado,
summer - A2 scenario
GFDL
HadCM3
CCSM
CGCM
70
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

71
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
72
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

73
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