Climate and Energy in California

1. Climate and Energy in California David W. Pierce Tim P. Barnett Eric Alfaro Alexander Gershunov Climate Research Division Scripps Institution of Oceanography La Jolla, CA

2. How we got started: a typical climate change result What does this mean to us? IPCC, 2001

3. Effect of Climate Change on Western U.S. • Large and growing population in a semi-arid region • How will it impact water resources? • Use an “end-to-end” approach

4. Project overview Tim Barnett, SIO; R. Malone, LANL; W. Pennell, PNNL; A. Semtner, NPS; D. Stammer, SIO; W. Washington, NCAR

5. Step 1 • Begin with current state of global oceans

6. Why initialize the oceans? • That’s where the heat has gone Data from Levitus et al, Science, 2001

8. Step 2 • Estimate climate change due to emissions

9. Global Climate Change Simulation • Parallel Climate Model (PCM) • Business as Usual Scenario (BAU) • 1995-2100 • 5 ensemble members

10. How well does the PCM work over the Western United States? Dec-Jan-Feb total precipitation (cm)

11. Step 3 • Downscaling and impacts

12. Why downscale? Global model (orange dots) vs. Regional model grid (green dots)

13. How good is downscaling? El Nino rainfall simulation Observations Standard reanalysis Downscaled model Ruby Leung, PNNL

14. Change in California snowpack

15. Projected change by 2050

16. River flow earlier in the year

17. Runoff already coming earlier

18. Columbia Basin Options Hydropower Or Salmon

19. Los Angeles water shortage Christensen et al., Climatic Change, to appear

20. Miss water treaty obligations to Mexico Christensen et al., Climatic Change, to appear

21. More wildfires 100% more acres burned in 2100

22. Less time for Salmon to reproduce Now: Future: Lance Vail, PNNL

24. Climate change conclusions • A reduction of winter snowpack. Precipitation more likely to fall as rain, and what snow there is melts earlier in the year. • River flow then comes more in winter/spring than in spring/summer – implications for wildfires, agriculture, recreation, and how reservoirs are managed. • Will affect fish whose life cycle depends on the timing of water temperature and spring melt. • Will also change salinities in the San Francisco bay.

25. More heat waves Dan Cayan and Mike Dettinger, Scripps Inst. Oceanography

26. August daily high temperature, Sacramento, CA On a warm summer afternoon, 40% of all electricity in California goes to air conditioning

27. California Energy Project Objective: Determine the economic value of climate and weather forecasts to the energy sector

28. Climate & weather affect energy demand Source:www.caiso.com/docs/0900ea6080/22/c9/09003a608022c993.pdf

29. …and also energy supply Typical effects of El Nino: CA hydro Green et al., COAPS Report 97-1

30. Project Overview Scripps Inst. Oceanography University of Washington Georgia Inst. Tech Academia California Energy Commission California ISO PacifiCorp San Diego Gas & Elec. SAIC State Partners Industrial Partners

31. Why aren’t climate forecasts used? • Climate forecasts are probabilistic in nature – sometimes unfamiliar to the user

32. What climate forecasts mean

33. Why aren’t climate forecasts used? • Climate forecasts are probabilistic in nature – sometimes unfamiliar to the user • Lack of understanding of climate forecasts and their benefits • Language and format of climate forecasts is hard to understand – need to be translated for end-users • Aversion to change – easier to do things the traditional way

34. 1. California "Delta Breeze" • An important source of forecast load error (CalISO) • Big events can change load by 500 MW (>1% of total) • Direct cost of this power: $250K/breeze day (~40 days/year: ~$10M/year) • Indirect costs: pushing stressed system past capacity when forecast is missed!

35. NO delta Breeze Sep 25, 2002: No delta breeze; winds carrying hot air down California Central valley. Power consumption high.

36. Delta Breeze Sep 26, 2002: Delta breeze starts up; power consumption drops >500 MW compared to the day before!

37. Weather forecasts of Delta Breeze 1-day ahead prediction of delta breeze wind speed from ensemble average of NCEP MRF, vs observed.

38. Statistical forecast of Delta Breeze (Also uses large-scale weather information) By 7am, can make a determination with >95% certainty, 50% of the time

39. Delta Breeze summary • Using climate information can do better than dynamic weather forecasts • Possible savings of 10 to 20% in costs due to weather forecast error. Depending on size of utility, will be in range of high 100,000s to low millions of dollars/year.

40. 2. Load demand management • Induce customers to reduce electrical load on peak electrical load days • Prediction challenge: call those 12 days, 3 days in advance • Amounts to calling weekdays with greatest "heat index" (temperature/humidity)

41. Why shave peak days? http://www.energy.ca.gov/electricity/wepr/2000-07/index.html

42. Price vs. Demand http://www.energy.ca.gov/electricity/wepr/1999-08/index.html

43. July Average = 2916 MW

44. July Average = 2916 MW Top days = 3383 MW (16 % more than avg)

45. Peak day electrical load savings • If knew electrical loads in advance: 16% • With event constraints: 14% (Load is relative to an average summer afternoon)

46. July Average = 2916 MW

47. July Average = 2916 MW Warm days = 3237 MW (11 % more than avg)

48. Peak day electrical load savings • If knew electrical loads in advance: 16% • With event constraints: 14% • If knew temperature in advance: 11% (Load is relative to an average summer afternoon)

49. What can climate analysis say?

50. Peak day electrical load savings • If knew electrical loads in advance: 16% • With event constraints: 14% • If knew temperature in advance: 11% • Super simple scheme (24C, 0.5): 6% (Load is relative to an average summer afternoon)