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. Climate Models Global forecast models Regional models Global models in 5 yrs

3. “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

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 (G=B1, H=A1B, I=A2) 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% –Imports32% –Recycled1% –Surface8% –Desalter2%

11. Results Groves 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 Hypothesis: The distribution of urban vegetation is an important intermediary between patterns of human settlement and local temperature. Urban Vegetation Distribution Regional Temperature Distribution Rapid Urbanization Harlan et al., Arizona State Urban Heat Study (under way) Neighborhood Income Distribution

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. 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

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 Information in Adaptation Planning LocationEmissionsClimate Models Downscaling Used NotesReference Gulf Coast3 SRESAR4 MultipleNoneSAP 4.7 California2 SRES2 GCMsSimpleCayan et al. 2008 Maryland2 SRES17 AR4SimpleBoesch et al. 2008 Colorado River 2 SRES19 AR4NoneSeager et al. 2007 New York City 3 SRES A2, A1B, B1 Multiple – ranges of changes in key variables SimpleSea level rise scenarios - mod of IPCC 2007 NPCC, 2009 King County2 SRES – A2, B1 10 AR4SimpleMote et al., 2005 Miami Dade County None Sea level rise scenarios ??

18. 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?

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 Snowpack GCM under-predicted and misplaced snow Regional SimulationGlobal Simulation

21. Climate Change Signals TemperaturePrecipitation PCM RCM Leung et al., 2004 RCM (MM5) nested in PCM PCM = GCM

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. Model 300km Global Model 25km Regional Model 50km Regional Model Observed WINTER PRECIPITATION OVER GREAT BRITAIN (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. RCM GCM ensemble member RCM RCM ensemble member GCM ensemble member GCM ensemble member RCM ensemble member RCM ensemble member scenario The Future Mother Of All Ensembles

27. CORDEX domains NARCCAP CLARIS ENSEMBLES RCMIP

28. CORDEX Phase I experiment design Model Evaluation Framework Climate Projection Framework ERA-Interim BC 1989-2007 Multiple AOGCMs RCP4.5, RCP8.5 1951-2100 1981-2010, 2041-2070, 2011-2040 Multiple regions (Initial focus on Africa) 50 km grid spacing Regional Analysis Regional Databanks

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

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

39. Regions Analyzed Boreal forest Pacific coast California coast Deep South Great Lakes Maritimes Upper Mississippi River

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. 1982-83 El Nino 1997-98 El Nino multi-year drought Monthly time series of precipitation in coastal California small spread, high skill

42. Correlation with Observed Precipitation - Coastal California Model Correlation HadRM30.857 RegCM30.916 MM50.925 RSM0.945 CRCM0.946 WRF0.918 Ensemble 0.947 Ensemble mean has a higher correlation than any model All models have high correlations with observed monthly time series of precipitation.

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 Model Correlation HadRM30.489 RegCM30.231 MM50.343 RSM0.649 CRCM0.649 WRF0.513 Ensemble0.640 Two models (RSM and CRCM) perform much better. These models inform the domain interior about the large scale. Ensemble (black curve)

45. Monthly Time Series - Deep South Model Correlation HadRM30.489 RegCM30.231 MM50.343 RSM0.649 CRCM0.649 WRF0.513 Ensemble0.640 RSM+CRCM0.727 A “mini ensemble” of RSM and CRCM performs best in this region. Ensemble (black curve)

46. A2 Emissions Scenario GFDLCCSMHADCM3CGCM3 1971-2000 current 2041-2070 future Provide boundary conditions MM5 Iowa State/ PNNL RegCM3 UC Santa Cruz ICTP CRCM Quebec, Ouranos HADRM3 Hadley Centre RSM Scripps WRF NCAR/ PNNL NARCCAP PLAN – Phase II CAM3 Time slice 50km GFDL Time slice 50 km

47. GCM-RCM Matrix GFDLCGCM3HADCM3CCSM MM5 XX1 RegCM X1**X CRCM X1** X HADRM XX1** RSM X1 X WRF XX1 *CAM3 X *GFDL X** 1 = chosen first GCM *= time slice experiments Red = run completed ** = data loaded 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 Temperature UK Models

51. Change in Winter Temperature Canadian Models Global Model Regional Model

52. Change in Summer Temperature UK Models

53. Change in Summer Temperature Canadian Models

54. Change in Winter Precip UK Models

55. Change in Winter Precip Canadian Models

56. Change in Summer Precip UK Models

57. Change in Summer Precip Canadian Models

58. Summer Temp Changes 2051-2070—1980-1999

59. A2 Emissions Scenario GFDL AOGCM NCAR CCSM Global Time Slice / RCM Comparison at same resolution (50km) Six RCMS 50 km GFDL AGCM Time slice 50 km CAM3 Time slice 50km compare

60. GFDL CM2.1 GFDL AM2.1 Future-current Summer Temperatures

61. RegCM3 in GFDL Change in Summer Temperature

63. RegCM3 in GFDL Change in Winter Temperature

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 GFDLHadCM3CCSMCGCM

69. Probability of temperature change for Colorado, summer - A2 scenario GFDL HadCM3CCSMCGCM

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 1/06 6/09 2/10 12/07 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

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@ucar.edu

73. End