2. Climate Change, Water, and Energy in the U.S. West David W. Pierce Tim P. Barnett Climate Research Division, Scripps Institution of Oceanography, La Jolla, CA Funding by NOAA & DOE

3. IPCC, 2001

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

5. Why initialize the oceans? That’s where the heat has gone Data from Levitus et al, Geophys Res Lett, 2005

9. Global to regional view Global model (orange dots) vs. Regional model grid (green dots)

10. How good is downscaling? El Nino rainfall simulation ObservationsDownscaled modelStandard reanalysis Ruby Leung, PNNL

11. Change in California snowpack

13. River flow earlier in the year

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

15. More wildfires 100% more acres burned in 2100 Anthony Westerling,SIO

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

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

20. Climate affects energy supply… Green et al., COAPS Report 97-1 Typical effects of El Nino CA hydro

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

23. Climate/Energy Case Studies Worked with energy industry participants Three case studies: 1.California Delta Breeze (SF bay area) 2.Irrigation pumps in agricultural areas 3.North Pacific Oscillation and winter heating

24. Case 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!

25. NO delta Breeze (Sept 25, 2002)

26. Delta Breeze (Sept 26, 2002)

27. How well does the forecast do? Statistical forecast Hits Predicted: YES Observed: YES 52% Predicted: NO Observed: NO 44% Misses Predicted: NO Observed: YES 1% Predicted: YES Observed: NO 3% Standard forecast Hits Predicted: YES Observed: YES 52% Predicted: NO Observed: NO 32% Misses Predicted: NO Observed: YES 9% Predicted: YES Observed: NO 8%

28. Delta Breeze summary 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.

29. Case 2. Irrigation pump loads Electricity use in Pacific Northwest strongly driven by irrigation pumps When will the pumps start? What will total seasonal use be?

30. Irrigation pump electrical use

31. Pump start date Eric Alfaro, SIO

32. Total use over summer Idaho Falls, ID

33. Total load affected by soil moisture Eric Alfaro, SIO Wet Dry

34. Irrigation load summary Buying power contracts 2 months ahead of a high-load summer saves $25/MWh (over spot market price) Use: about 100,000 MWh Benefit of 2 month lead time summer load forecast: $2.5 M

35. 3. NPO and winter heating

36. …and demand Positive NPO Negative NPO Difference is about 150 HDD, or 5% of total HDD

39. Los Angeles water shortage Christensen et al., Climatic Change, 2004

40. What climate forecasts mean

41. Economic value of climate forecasts to the energy sector 1.Improved bay area and delta breeze forecasts: $100K’s to low $millions/yr 2.Peak day load management: ~$1-10M/yr 3.Pump loads: ~$2M/yr 4.Pacific SSTs: benefits of the information might include risk reduction, improved reliability, and improved planning 5.Hydropower: better water management, reduced costs

42. Case 2. Peak demand days Induce customers to reduce electrical load on peak electrical load days

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

44. Forecaster’s job Call those 12 high use days, 3 days in advance Amounts to calling weekdays with greatest "heat index" (temperature/humidity)

45. Potential peak day savings Average summer afternoon: 3000 MW Top 12 summer afternoons: 3480 MW (+16%) With PUC constraints: 3420 MW (+14%) Top 12 warmest afternoons: 3330 MW (+11%)

46. What can climate analysis say?

47. Potential peak day savings Average summer afternoon: 3000 MW Top 12 summer afternoons: 3480 MW (+16%) With PUC constraints: 3420 MW (+14%) Top 12 warmest afternoons: 3330 MW (+11%) Super simple scheme: 3180 MW (+6%)

48. Peak day summary Might ultimately be a real-time program –Driven by "smart" electric meters –Main benefit would be avoided cost of peaker generation plants ~$12M/yr. Until then, climate prediction: –Far less deployment cost –Cost of avoided procurement ~$1.3M/yr -> Climate analysis can give expected benefits to a program

49. 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. Climate change conclusions

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

51. El Nino/La Nina

52. Why does that affect other places? Global atmospheric pressure pattern “steers” weather Horel and Wallace, 1981

53. Climate change Some of it is straightforward

54. Other parts are harder Clouds have competing effects

55. How good is the Hydrological Model? Andrew Wood, Univ. of Washington

56. Predicted change by 2050

58. Columbia River flow Andrew Wood, Univ. of Washington

59. The problem: Proposal to breach 4 Snake River dams to improve salmon habitat Those dams provide 940 MW of hydropower generation

61. Historical Global Temperatures

62. MSU (microwave sounding unit)

63. A difficult data set…

64. Problem: Orbit decay

65. MSU versus Jones

66. Paleo temperature history Mann et al, 2001

67. Effect of Economic Assumptions IPCC, 2001

68. Natural vs. Human Influences IPCC, 2001

69. Predicting summer temperature based on spring temperature

70. Extreme events Same temperature threshold (e.g. 95 °F) => Same percentile threshold (e.g. 95th) =>

71. Spring SST predicting summer temperatures CDD Tmax-95 th percentile

72. Relationship PDO => California Summertime Temperatures Correlations, Mode 1- Tmean, JJA => Correlations, Mode 1-PSST, MAM

73. Contingency Analysis (conditional probabilities):  = 0.01 => ***, 0.05 => **, 0.10 => *

74. Step 2: Apply to soil/streamflow model Nathalie Voisin et al., Univ. Washington, 2004

75. Strong year to year variability

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

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

78. Summer temperature, NPO above normal in spring Possible benefits: better planning, long term contracts vs. spot market prices

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

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

81. Why the NPO matters Higher than usual pressure associated with the NPO… generates anomalous winds from the north west… …which bring more cold, arctic air into the western U.S. during winter

82. 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