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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 IPCC, 2001 Tim Barnett, SIO; R. Malone, LANL; W. Pennell, PNNL; A. Semtner, NPS; D. Stammer, SIO; W. Washington, NCAR Why initialize the oceans? • That’s where the heat has gone Data from Levitus et al, Geophys Res Lett, 2005 Global to regional view Global model (orange dots) vs. Regional model grid (green dots) How good is downscaling? El Nino rainfall simulation Observations Standard reanalysis Downscaled model Ruby Leung, PNNL Change in California snowpack River flow earlier in the year Less time for Salmon to reproduce Now: Future: Lance Vail, PNNL More wildfires 100% more acres burned in 2100 Anthony Westerling,SIO More heat waves Dan Cayan and Mike Dettinger, Scripps Inst. Oceanography Climate & weather affect energy demand Source: www.caiso.com/docs/ 0900ea6080/22/c9/09003a608022c993.pdf Climate affects energy supply… CA hydro Typical effects of El Nino Green et al., COAPS Report 97-1 California Energy Project Objective: Determine the economic value of climate forecasts to the energy sector 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 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! NO delta Breeze (Sept 25, 2002) Delta Breeze (Sept 26, 2002) How well does the forecast do? Statistical forecast Standard forecast Hits Hits Predicted: YES Observed: YES 52% Predicted: YES Observed: YES 52% Predicted: NO 44% Predicted: NO Observed: NO 32% Observed: NO Misses Misses Predicted: NO Observed: YES 1% Predicted: NO Observed: YES 9% Predicted: YES Observed: NO 3% Predicted: YES Observed: NO 8% 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. 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? Irrigation pump electrical use Pump start date Eric Alfaro, SIO Total use over summer Idaho Falls, ID Total load affected by soil moisture Dry Wet Eric Alfaro, SIO 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 3. NPO and winter heating …and demand Positive NPO Negative NPO Difference is about 150 HDD, or 5% of total HDD Los Angeles water shortage Christensen et al., Climatic Change, 2004 What climate forecasts mean Economic value of climate forecasts to the energy sector Improved bay area and delta breeze forecasts: $100K’s to low $millions/yr Peak day load management: ~$1-10M/yr Pump loads: ~$2M/yr Pacific SSTs: benefits of the information might include risk reduction, improved reliability, and improved planning 5. Hydropower: better water management, reduced costs 1. 2. 3. 4. Case 2. Peak demand days • Induce customers to reduce electrical load on peak electrical load days Price vs. Demand Forecaster’s job • Call those 12 high use days, 3 days in advance • Amounts to calling weekdays with greatest "heat index" (temperature/humidity) 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%) What can climate analysis say? 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%) 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 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 How well does the PCM work over the Western United States? Dec-Jan-Feb total precipitation (cm) El Nino/La Nina Why does that affect other places? Global atmospheric pressure pattern “steers” weather Horel and Wallace, Climate change Some of it is straightforward Other parts are harder Clouds have competing effects How good is the Hydrological Model? Andrew Wood, Univ. of Washington Predicted change by 2050 Columbia River flow Andrew Wood, Univ. of Washington The problem: • Proposal to breach 4 Snake River dams to improve salmon habitat • Those dams provide 940 MW of hydropower generation Historical Global Temperatures MSU (microwave sounding unit) A difficult data set… Problem: Orbit decay MSU versus Jones Paleo temperature history Mann et al, 2001 Effect of Economic Assumptions IPCC, 2001 Natural vs. Human Influences Predicting summer temperature based on spring temperature Extreme events Same temperature threshold (e.g. 95 °F) => Same percentile threshold (e.g. 95th) => Spring SST predicting summer temperatures CDD Tmax-95th percentile Relationship PDO => California Summertime Temperatures 0 20 40 60 Correlations, Mode 1-PSST, MAM -1.0 0.0 1.0 150 Correlations, Mode 1Tmean, JJA => 200 250 300 Contingency Analysis (conditional probabilities): < 736 CDD-JJA > 856 BN N AN BN 53** 29 18* N 29 42 29 AN 18* 29 53** BN N AN < 331 BN 53** 35 12*** CDD-JJA N 35 36 29 > 414 AN 12*** 29 59*** BurbankGlendalePasadena PDO MAM San Jose PDO MAM = 0.01 => ***, 0.05 => **, 0.10 => * Step 2: Apply to soil/streamflow model Strong year to year variability Weather forecasts of Delta Breeze 1-day ahead prediction of delta breeze wind speed from ensemble average of NCEP MRF, vs observed. Statistical forecast of Delta Breeze (Also uses large-scale weather information) By 7am, can make a determination with >95% certainty, 50% of the time Summer temperature, NPO above normal in spring Possible benefits: better planning, long term contracts vs. spot market prices Miss water treaty obligations to Mexico Christensen et al., Climatic Change, to appear Why shave peak days? 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 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