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

Linda O. MearnsNational Center for Atmospheric Research

PCC/CIG

University of Washington Seattle, Washington

October 27, 2009

Climate Models Global forecast models

Regional models Global models in 5 yrs

“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)

The ‘Mismatch’ of Scale Issue

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?

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

Examples of Resolutions Used in Recent Climate Impacts

Studies

• Ecology

• Water Resources

• Heat Stress

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)

Sample Results

‘Predictions’ of climate-induced species turnover for three emissions scenarios (G=B1, H=A1B, I=A2) for 2071-2100.

Conclusion: projected severe faunal change – even lowest scenarios indicates substantial change in biodiversity

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

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%

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

Heat Stress Study Regional Relationships:

Income, Vegetation, and Temperature

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

Urban VegetationDistribution

Regional Temperature Distribution

Rapid Urbanization

Harlan et al., Arizona State Urban Heat Study (under way)

Neighborhood Income

Distribution

WRF Model - Multiple nestingSimulations:• 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 supercomputerSimulations for 180 days per summer: ~ 22.5 days

• 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)

Use of Regional Climate Model Results

for Impacts Assessments

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

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?

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

??

NYC Adaptation Plan• Climate change information taken from

global climate models – ranges given for different decades (e.g., 1.5 – 3°F 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?

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)

Global and Regional Simulations of Snowpack

GCM under-predicted and misplaced snow

Regional Simulation Global Simulation

Climate Change SignalsTemperature Precipitation

PC

MR

CM

Leung et al., 2004

RCM (MM5) nested in PCM

PCM = GCM

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

300km Global ModelModel

25km 25km Regional Regional ModelModel

50km 50km Regional Regional ModelModel

ObservedObserved

WINTER PRECIPITATION OVER GREAT BRITAIN

(HC models)

R. Jones : UKMO

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)

Oleson et al., 2007, Suitability for Maize cultivationBased 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

RCM RCM

GCM

ensemble

member

RCM

RCM ensemblemember

GCM GCMGCM

GCM

ensemble

member

GCM

ensemble

member

RCM ensemblemember

RCM ensemblemember

scenarioscenario scenario

The FutureThe Future

Mother Of All Ensembles

CORDEX domainsCORDEX domains

NARCCAPNARCCAP

CLARISCLARIS

ENSEMBLESENSEMBLES

RCMIPRCMIP

CORDEX Phase I experiment design CORDEX Phase I experiment design

Model Evaluation Model Evaluation FrameworkFramework

Climate ProjectionClimate ProjectionFrameworkFramework

ERA-Interim BC ERA-Interim BC 1989-20071989-2007

Multiple AOGCMsMultiple AOGCMs

RCP4.5, RCP8.5RCP4.5, RCP8.5

1951-21001951-21001981-2010, 2041-2070, 2011-20401981-2010, 2041-2070, 2011-2040

Multiple regions (Initial focus on Africa)Multiple regions (Initial focus on Africa)50 km grid spacing50 km grid spacing

Regional AnalysisRegional AnalysisRegional DatabanksRegional Databanks

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

The North American Regional Climate Change Assessment Program (NARCCAP)

•Exploration of multiple uncertainties in regional model and global climate model regional projections.

•Development of multiple high resolution regionalclimate 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

Initiated in 2006, it is an international program that will servethe climate scenario needs of the United States, Canada, and northern Mexico.

www.narccap.ucar.edu

NARCCAP - TeamLinda 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

NARCCAP Domain

Organization of Program

• Phase I: 25-year simulations using NCEP-Reanalysis boundary conditions (1979—2004)

• 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).

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

Regions Analyzed

Boreal forest

Pacific coast

California coast

Deep South

Great LakesMaritimes

Upper Mississippi

River

Coastal CaliforniaCoastal 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

0

3

6

9

12

15

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

mm

/day

RCM3 MM5IECPC CRCMWRFP HRM3Observed (GPCC) Observed (UDEL)Observed (CRUT) Ensemble

1982-83 El Nino

1997-98 El Nino

multi-year drought

Monthly time series of precipitation in coastal California

small spread, high skill

Correlation with Observed Precipitation - Coastal California

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

All models have high correlations with observed monthly time series of precipitation.

Deep SouthDeep 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.

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

Two models (RSM and CRCM) perform much better. These models inform the domain interior about the large scale.

0

3

6

9

12

15

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

mm

/da

y

RCM3 MM5I ECPCCRCM WRFP HRM3Observed (GPCC) Observed (UDEL) Observed (CRUT)Ensemble

Ensemble (black curve)

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

RSM+CRCM 0.727

A “mini ensemble” of RSM and CRCM performs best in this region.

0

3

6

9

12

15

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

mm

/da

y

RCM3 MM5I ECPCCRCM WRFP HRM3Observed (GPCC) Observed (UDEL) Observed (CRUT)Ensemble

Ensemble (black curve)

A2 Emissions Scenario

GFDL CCSMHADCM3CGCM3

1971-2000 current 2041-2070 futureProvide boundary conditions

MM5Iowa State/PNNL

RegCM3UC Santa CruzICTP

CRCMQuebec,Ouranos

HADRM3Hadley Centre

RSMScripps

WRFNCAR/PNNL

NARCCAP PLAN – Phase II

CAM3Time slice

50km

GFDLTime slice

50 km

GCM-RCM Matrix

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 *= time slice experiments Red = run completed ** = data loaded

AOGCMS

RCMs

Phase II (Climate Change) Results

Temperature and precipitation changes with model agreement

(2080-2099 minus 1980-1999) A1B Scenario

Change in Winter TemperatureUK Models

Change in Winter TemperatureCanadian Models

Global Model Regional Model

Change in Summer TemperatureUK Models

Change in Summer TemperatureCanadian Models

Change in Winter PrecipUK Models

Change in Winter PrecipCanadian Models

Change in Summer PrecipUK Models

Change in Summer PrecipCanadian Models

Summer Temp Changes 2051-2070—1980-1999

A2 Emissions Scenario

GFDLAOGCM

NCARCCSM

Global Time Slice / RCM Comparisonat same resolution (50km)

Six RCMS50 km

GFDLAGCM

Time slice50 km

CAM3Time slice

50km

compare compare

GFDL CM2.1

GFDL AM2.1

Future-current Summer Temperatures

RegCM3 in GFDLChange in Summer Temperature

RegCM3 in GFDLChange in Winter Temperature

RegCM3 in GFDL% Change Precip - Winter

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

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

Probability of temperature change for Colorado, Spring- A2 scenario

GFDL HadCM3 CCSMCGCM

Probability of temperature change for Colorado, summer - A2 scenario

GFDLHadCM3CCSMCGCM

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

NARCCAP Project Timeline

1/066/09

2/1012/07

AOGCM Boundaries available

9/07

Phase 1

Future climate 1

9/08

Current climate1

Current and Future 2 Project Start

Time slices

Archiving Procedures - Implementation

Phase IIb

Phase IIa

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@ucar.edu

End