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)
Statistically relating large scale climate features (e.g. 500 mb heights) to local climate (e.g daily monthly temperature at a point)
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
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
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 800000 people
May add 300000 by 2025
Current water sources include
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
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
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
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)
Wotton et al. 1998 (Canada Boreal Forest)
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
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
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
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 (No Transcript) 36 (No Transcript) 37 (No Transcript) 38 (No Transcript) 39 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
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 (No Transcript) 63 RegCM3 in GFDLChange in Winter Temperature 64 (No Transcript) 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)
Statistically relating large scale climate features (e.g. 500 mb heights) predictors to local climate (e.g daily monthly temperature at a point) predictands
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
To sign up as user go to web site contact Seth McGinnis
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