Transcript DWR_2005-08-23wide

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
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29
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CDD-JJA
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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