1. Pacific Northwest Hydrologic and Climate Change Scenarios for the 21st Century A Brief Introduction to New Products and Overview of Downscaling Approaches Climate Science in the Public Interest a webinar sponsored by the Climate Impacts Group University of Washington March 24, 2010 1:00-2:00pm

2. Webinar Tips Mute phones to reduce background noise (*6 or “mute” button). Do not put call on “hold” if your organization has hold music. Type questions in the chat box (lower left). We will track the questions and answer as time permits at the end of the presentation. Additional opportunities for Q&A: Post-webinar survey Email CIG: cig@uw.educig@uw.edu Presentation will be posted on the webinar meeting page on the CIG web page (www.cses.uw.edu/cig)www.cses.uw.edu/cig

3. Purpose Who is the Climate Impacts Group? Part 1: Introduction to the Columbia Basin Climate Change Scenarios Project products and statistical downscaling methods Part 2: A broader look at the role of dynamical regional climate modeling in scenario development at the CIG Future workshops, webinar opportunities

4. Collective expertise includes (but is not limited to): Statistical and dynamical downscaling of global climate model projections Macro and fine scale hydrologic modeling Water resources impacts assessment Terrestrial and aquatic ecosystems impacts assessment Adaptation planning and outreach ObjectivesIncrease regional resilience to climate variability and change Produce science useful to (and used by!) the decision making community An integrated research team studying the impacts of climate variability and climate change in the PNW and western US The Climate Impacts Group

5. Part 1 Responding to Evolving Stakeholder Needs for 21 st Century Hydrologic Scenarios: An Overview of the Columbia Basin Climate Change Scenarios Project Alan F. Hamlet Marketa McGuire Elsner CSES Climate Impacts Group Department of Civil & Environmental Engineering University of Washington Climate Science in the Public Interest

6. Climate Change Planning Needs are Evolving Stakeholder requests: 1. Address diverse stakeholder planning needs (terrestrial and aquatic ecosystems, water management, human health, energy, etc.) 2. Provide comprehensive coverage over large geographic areas using consistent methods 3. Increase spatial resolution -- address both large-scale and small-scale planning efforts in a consistent manner 4. Increase temporal resolution -- address changes at daily timescales and assess changes in hydrologic extremes 5. Quantify uncertainties in future projections

7. The Columbia Basin Climate Change Scenarios Project This 2-year research project (finalizing Spring 2010) is designed to provide a comprehensive suite of 21 st century hydroclimatological scenarios for the Columbia River basin and coastal drainages in OR and WA Collaborative Partners: WA State Dept. of Ecology (via HB 2860) Bonneville Power Administration Northwest Power and Conservation Council Oregon Water Resources Department BC Ministry of the Environment

8. 297streamflowsites Provide a wide range of products to address multiple stakeholder needs Increase spatial and temporal resolution Provide a large ensemble of climate scenarios to assess uncertainties Address hydrologic extremes (e.g. Q100 and 7Q10) Project Goals and Objectives

9. http://www.hydro.washington.edu/2860/ Draft

10. Overview of Methods

11. Primary Meteorological Stations Used to Create Spatial Dataset* (1915-2006) *1/16 spatial resolution & daily timestep

12. Climate Impacts Group 2009, WA Assessment, Ch. 1 http://cses.washington.edu/cig/res/ia/waccia.shtml Climate Change Scenarios Figure shows change compared with 1970 -1999 average IPCC AR 4 Emissions Scenarios: A1BMedium High B1Low Projected change in mean annual T Projected change in mean annual P

13. Downscaling Relates the “Large” to the “Small” ~200 km (~125 mi) resolution ~5 km (~3 mi) resolution

14. Statistical Downscaling Approaches Simple, easy to explain “snapshot” of average future conditions Incorporates realistic historical daily time series and spatial variability Provides 91 years of historical variability combined with changes in T and P for each future time frame and emissions scenario Projections are easily relate to stakeholder knowledge of historical impacts Composite Delta Method Strengths: Limitations: Incorporates only average changes in mean monthly T and P, not extremes Changes are assumed to be the same throughout the region

15. Statistical Downscaling Approaches Incorporates more information from the monthly GCMs, including altered monthly T and P variability in space and time Produces a transient (i.e. continually varying) daily time series for 150 years (1950-2098+) Simulates rates of change in hydrologic variables Offers flexible time period of analysis (one run gives all future time periods) Bias Corrected and Spatially Downscaled (BCSD) Strengths: Limitations: Quality of downscaled realizations is dependent on GCM performance – What you get out is only as good as what you put in! Ensemble analysis needed to account for decadal P variability due to relatively small sample size in future time slices (~30 years) Daily time series characteristics are not suitable for some kinds of analyses (e.g. analysis of hydrologic extremes)

16. Statistical Downscaling Approaches Combines the strengths of the Composite Delta and BCSD methods, while avoiding most of the weaknesses of both Incorporates more information from the monthly GCMs, including altered spatial patterns of T and P changes Incorporates realistic historical daily time series and spatial variability Provides 91 years of historical variability combined with changes in T and P for each future time frame and emissions scenario Arguably the best method for evaluating hydrologic extremes (floods and low flows) Hybrid Delta Method Strengths: Limitations: Quality of downscaled realizations is dependent on GCM performance (spatial patterns only) Constrained by the time series behavior in the historic record

17. Available PNW Scenarios 2020s – mean 2010-2039; 2040s – mean 2030-2059; 2080s – mean 2070-2099 Downscaling Approach A1B Emissions Scenario B1 Emissions Scenario Hybrid Delta hadcm cnrm_cm3 ccsm3 echam5 echo_g cgcm3.1_t47 pcm1 miroc_3.2 ipsl_cm4 hadgem1 2020s10 2040s10 2080s10 Transient BCSD hadcm cnrm_cm ccsm3 echam5 echo_g cgcm3.1_t47 pcm1 1950-2098+77 Delta Method composite of 10 2020s11 2040s11 2080s11

18. Snow Model Schematic of VIC Hydrologic Model Sophisticated, fully distributed, physically based hydrologic model Widely used globally in climate change applications 1/16 Degree Resolution (~5km x 6km or ~ 3mi x 4mi) General Model Schematic

19. Sample Results Using Different Statistical Downscaling Approaches

20. http://www.hydro.washington.edu/2860/ Hydrologic Products Draft

21. Changing Watershed Classifications: Transformation From Snow to Rain Based on Composite Delta Method scenarios (multimodel average change in T & P) Historical period includes 1916-2006 water years (Oct-Sep) Map: Rob Norheim

22. Trend (Days per Decade) * 50mm SWE Plotting Threshold Linear trend for the ECHAM 5 A1B Scenario downscaled using the transient BCSD Downscaling Method Trends in Date of Peak SWE

23. http://www.hydro.washington.edu/2860/products/sites/?site=6092

24. Snow and Runoff Summary (Cispus River near Randle) Scenario EnsemblesEnsemble MeanHistorical Mean

25. Draft study results (to be finalized Spring 2010) are already being used and evaluated by a wide range of stakeholders including: USGS Bonneville Power Administration U.S. Bureau of Reclamation U.S. Army Corps of Engineers U.S. Forest Service U.S. Fish and Wildlife Service Boise Aquatic Research Laboratory National Marine Fisheries Science Center Who’s Using the Data?

26. Next steps… Extending the approach to additional western U.S. watersheds in partnership with: US Forest Service US Fish and Wildlife Service Boise Aquatic Sciences Lab Trout Unlimited

27. Eric Salathé JISAO Climate Impacts Group University of Washington Ruby Leung & Qian Fu PNNL Yongxin Zhang CIG, NCAR Cliff Mass UW Part 2 Regional Climate Modeling for Impacts Applications in the US Pacific Northwest

28. Downscaling and Regional Climate Modeling Statistical Downscaling Maps the climate change signal from a global model onto the observed patterns Computationally efficient Can tune to observed climate Preserves uncertainty in Global Climate Models Cannot represent fine-scale patterns of climate change Regional Climate Models (“Dynamic Downscaling”) Extend the physical modeling of the climate system to finer spatial scales Computationally demanding Cannot correct bias in global model Adds to uncertainty from Global Climate Models 12-50 km or ~7-32 mi Global Climate Model Regional Climate Model (WRF) Statistical Downscaling (BCSD) Bias Correction Stat Downscale 6-hourlyMonthly Time Disaggregation Hourly Output Daily Output 100-200 km 6 km or ~3.7 mi

29. 150-km GCM High resolution is needed for regional studies Washington Oregon Idaho Cascade Range Rocky Mountains Snake Plain Olympics Global models typically have 100-200 km (62-124 mi.) resolution Cannot distinguish Eastern WA from Western WA No Cascades No land cover differences

30. 150-km GCM Washington Oregon Idaho Cascade Range Rocky Mountains Snake Plain Olympics Regional models typically have 12-50 km (7-32 mi) resolution 12 km WRF at UW/CIG Can represent major topographic features Can simulate small extreme weather systems Represent land surface effects at local scales 12-km WRF High resolution is needed for regional studies, cont’d

31. Why do we want to simulate the regional climate? Process studies  Topographic effects on temperature and precipitation  Extreme weather  Attribution of observed climate change  Land-atmosphere interactions Climate Impacts Applications  Streamflow and flood statistics  Water supply  Ecosystems  Human health  Air Quality

32. Regional Climate Modeling at CIG WRF Model (NOAH LSM) Resolution: 12 to 36 km (~7- 32 mi)  ECHAM5 forcing  CCSM3 forcing (A1B and A2 scenarios) HadRM Resolution: 25 km (~15 mi)  HadCM3 forcing

33. Statistical Downscaling CCSM3 Fall difference between 1990s and 2040s Low spatial detail for climate change signal °C % Temperature (°C) Precipitation (% change)

34. WRF “Dynamic Downscaling” CCSM3 Fall difference between 1990s and 2040s High spatial detail for climate change signal % Temperature (°C) Precipitation (% change) °C

35. Regional Effects and Applications

36. Land-Atmosphere Interactions Snow Cover ChangeTemperature Change Change in winter temperature (degrees C) Change in fraction of days with snow cover Wintertime Change from 1990s to 2050s Salathé et al. 2008

37. Land-Atmosphere Interactions Wintertime Change from 1990s to 2050s Salathé et al 2008 Solar Radiation

38. Example: Endangered Species Pika The American Pika depends on cold alpine climate Loss of snow accelerates warming Andrea J Ray, et al. Rapid-Response Climate Assessment to Support the FWS Status Review of the American Pika. U.S. Fish and Wildlife Service and the NOAA Earth System Research Laboratory Projected Changes to 2040s in Spring Pacific Northwest Snowpack

39. Extreme Precipitation Change from 1970-2000 to 2030-2060 in the percentage of total precipitation occurring when daily precipitation exceeds the 20 th century 95 th percentile Larger increase on windward slopes of Cascades, Columbia basin Smaller increase or decrease along Cascade crest

40. Example: Stormwater Management Rosenberg, E.A. et al, 2010. Climatic Change, in press. After Bias Correction, WRF results were used to compute flow in Thornton Creek, Seattle, to project storm-water impacts (David Hartley, Northwest Hydraulic Consultants ) Percent time exceedance of 50% of the peak 2-year flow for the period 1970–2000 using HSPF model with Bias- Corrected WRF simulations

41. Output Data for Applications Models WRF: Output provides the full hourly 3-D meteorological data needed as input for many applications models Air quality Puget Sound circulation Air Quality Modeling with WRF WRF Meteorology Air Quality Model (CMAQ) Anthropogenic Biogenic Emissions Human Health Risk Assessment

42. Example: Summertime Ozone Mortality King 1997-2006 King 2045-2055 Spokane 1997-2006 Spokane 2045-2055 Population1,758,2602,629,160424,636712,617 O3O3 20.726.535.541.6 Daily Mortality rate 0.0260.0330.0580.068 Deaths691323774 Jackson, JE. et al, 2010. Climatic Change, in press. Ozone projections from WRF-CMAQ and population projections are used to assess the future risk of mortality due to ozone exposure

43. Ongoing and Future Directions

44. Model Validation and Improvement 1. RCMs add significant local gradients to climate projections 2. If the magnitude or placement of gradients is wrong, this can degrade the climate projections Model Validation ‒ Both “forecast mode” and “climate mode” ‒ Compare to station observations ‒ Compare to satellite observations Model Improvement ‒ Modified snow model ‒ Optimizing model for accuracy and efficiency

45. WRF Ensemble Climate Projections To understand uncertainties in regional climate prediction Follows similar modeling and statistical analysis used in UW Ensemble Prediction System Goal: Use a large set of simulations to project probabilities for future changes WRF ensemble simulations underway now –4 members completed –12 km –100-year or 30-year duration

46. climateprediction.net Oxford University climateprediction.net Oxford University and Hadley Centre Global Climate Experiments Run by volunteers on personal computer Regional climateprediction.net Western US project at OCCRI and CIG Super ensemble of regional simulations Integrated global and regional modelling system 25 km grid Monthly-mean and statistical results Beta version soon Results over the next year

47. 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 50-km regional climate scenarios for use in impacts assessments. Evaluation of regional model performance over North America. www.narccap.ucar.edu 50-km Grid GFDLCGCM3HADCM3CCSM MM5 XX1 RegCM X1**X CRCM X1** X HADRM XX1 RSM X1 X WRF XX1 Red = run completed

48. Summary  Regional models can simulate unique local responses to climate change and improve on global models and statistical downscaling High spatial resolution is necessary to provide added value  Many applications require information only available from a regional climate model Some applications require additional bias correction or downscaling  Errors in regional models can amplify uncertainty Model validation is essential  Large uncertainties remain due to  Interannual variability  Global climate projections  Regional climate model differences An ensemble regional modeling approach is required

49. More to Come In the coming year, the CIG plans to host: Workshop(s) and/or webinars on the Columbia Basin Climate Change Scenarios Project A series of thematic webinars on global and regional climate variability and change, including webinars on specific sectors(e.g., water, forests) and planning for climate change Announcements will be sent via CIG listserve: climateupdate@uw.edu (see CIG home page) climateupdate@uw.edu

50. Thank You! Climate Science in the Public Interest Additional opportunities for Q&A: Post-webinar survey Email CIG: cig@uw.educig@uw.edu CIG Website: http://cses.uw.edu/cig/http://cses.uw.edu/cig/