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Transcript
Developing Climate Services for the Pacific
Northwest: the Role of Education, Outreach
and Partnerships
JISAO Center for Science in the Earth System
Climate Impacts Group
and Department of Civil and Environmental Engineering
University of Washington
March, 2004
Alan F. Hamlet and the Climate Impacts Group
Martyn Clark (UC Boulder, CIRES)
People in the Climate Impacts Group
PI: Edward L. Miles (human dimensions)
Principals:
Robert Francis (aquatic ecosystems)
Dennis P. Lettenmaier (hydrology and water resources)
Nathan Mantua (climate dynamics)
Philip W. Mote (state climatologist)
Lara Whitely Binder (education and outreach)
Richard Palmer (water resources management)
David L. Peterson (forests)
Amy K. Snover (integration and synthesis)
Climate Impact Science
The study of how climate, natural resources, and
human socio-economic systems affect each other
climate
CLIMATE
IMPACTS
SCIENCE
socioeconomic
systems
natural
resources
Identify Global/Regional Climate Drivers
Pacific Decadal Oscillation
El Niño Southern Oscillation
A history of the PDO
A history of ENSO
warm
warm
cool
1900 1910
1920
1930 1940 1950
1960 1970 1980
1990 2000
1900 1910
1920
1930 1940 1950
1960 1970 1980
1990 2000
Assemble and Analyze Observational Data Sets
Annual Flow at The Dalles 1858-1998
600000
5 events
500000
2 events
400000
300000
200000
100000
2000
1990
1980
1970
1960
1950
1940
1930
1920
1910
1900
1890
1880
1870
1860
1850
0
Identify Broad-Based Functional Relationships
Between Climate Forecasts and Natural Resources
450000
Cool
Cool
Warm
Warm
350000
300000
250000
200000
2000
1990
1980
1970
1960
1950
1940
1930
1920
1910
150000
1900
Apr-Sept Flow (cfs)
400000
Log10 mean flow, The Dalles, OR (cfs)
Extend Data Sets to Paleoclimatic Time Scales
5.5
red = observed, blue = reconstructed
5.4
5.3
5.2
5.1
5.0
1750
1775
1800
1825
1850
1875
Year
1900
1925
1950
1975
Source: Gedalof, Z., D.L. Peterson and Nathan J. Mantua. (in review). Columbia
River Flow and Drought Since 1750. Submitted to Journal of the American
Water Resources Association.
2000
Construct Models and
Analytical Tools
VIC
Hydrology Model
ColSim
Reservoir
Model
Construct Forecasting Systems
Ensemble
Streamflow
Forecast
ENSO
Climate
Forecast
PDO
Run Initialized
Hydrologic Model
Select Temperature and Precipitation Data
from Historic Record Associated with
Forecast Climate Category
Project Impacts Forwards in Time
VIC Simulations of April 1 Average Snow Water Equivalent
for Composite Scenarios (average of four GCM scenarios)
Current Climate
2020s
Snow Water Equivalent (mm)
2040s
Assess Forecast Skill, Error, Value
Oct
Dec
Nov
Jan ESP
Make Data Available to Users
http://www.ce.washington.edu/~hamleaf/climate_change_streamflows/CR_cc.htm
Role of Education, Outreach, Partnerships
1995
Many people in the climate prediction and
applications communities subscribed to a
“Field of Dreams” model:
“We will build it, they will come!”
1995-1996
Results from CIG Human
Dimensions Research:
1) Stakeholders were generally
unaware of existing climate
forecasting products and services.
2) Those products and services they
were aware of they did not use.
Conclusion:
“Field of Dreams” Model Likely to Fail:
Another Approach was Essential
Education
CIG
Outreach
Partnerships
Stakeholders
Strategies
• Continual networking to identify partnerships
• Workshops & surveys provide means for initial contact
• Capitalize on climate events
• Long-term commitment
CIG Annual Water Workshops
http://jisao.washington.edu/PNWimpacts/Workshops/Kelso2003/index.htm
Progress Resulting from Education and Outreach from the
CIG to the PNW Water Management Community
Familiarity with terminology and concepts associated with
interpreting climate forecasts
Understanding of the fundamental relationships between
climate variability and natural resources such as snowpack,
streamflow, and associated risks of droughts and floods.
Understanding of risks and uncertainties associated with
regional impacts of global warming.
Understanding of forecasting techniques incorporating
climate forecasts and information
Understanding opportunities for water management
applications
Progress Resulting from Education and Outreach from the
Water Management Community to the CIG
Understanding of the spatial and temporal scales at which
climate information and resource forecasts must be provided
to be useful.
Understanding that climate and resource forecasts must
function within a larger framework of management concerns.
Importance and role of institutional characteristics in the
process of bringing forecast innovations to bear on actual
resource management problems.
“Give us the raw data.” [transparent process]
Official products from authorized sources.
From Scientific Research to Operational
Climate Service Applications for Water Management
Track 1
Climate Forecasting Systems
New Idea:
Climate is
Predictable
Track 2
Hydrologic Forecasting Systems
and Water Management Applications
Track 3
Education and Outreach
Track 4
Technology Transfer
and Operational Design
Characteristics of “Early Adopters” of
Climate Forecasts
Seattle Public Utilities (CIG) [S/I forecasts, climate change]
Portland Water Bureau (CIG) [climate change]
•Strong incentives for effective management (particularly
Seattle)
•Small, centralized management agencies (autonomy)
•Ability to make changes in management decision processes
without incurring high costs
•Willingness on the part of leadership to invest in innovations
Characteristics of “Early Adopters” of
Climate Forecasts
Seattle City Light (3-Tier) [S/I forecasts]
Bonneville Power Administration (CIG) [S/I forecasts]
•Strong incentives for improved management (very large
economic benefits)
•Relatively sophisticated capacity to incorporate probabilistic
information, quantify risks, and manage outcomes successfully
•Seattle City Light is much farther ahead in terms of having
access to state of the art decision support tools
•BPA has been constrained by outdated decision support tools,
the size and complexity of the federal bureaucracy, and more
complicated system constraints.
Some Examples of Successful
Stakeholder Partnerships Outside the
PNW
•South Florida Water Management District [S/I forecasts]
•IRI Partnerships [S/I forecasts]
•CLIMAS Climate Outlook [S/I forecasts]
•SE Climate Consortium
•Colorado River Forecast Center (CDC, UC, CIRES) [S/I
forecasts]
Elements of a Successful Research-Operations Partnership
1) Successful identification of important problems on which researchers
and operational personnel can work collaboratively.
2) The Right People in the Right Places
• Upper level people in the academic and operational communities
willing to take the leadership role and to allocate resources to
collaborative projects.
• Individuals within the research community with appropriate technical
skills AND experience in finding creative solutions to operational
problems in a collaborative manner.
• Individuals within the operational agency with appropriate
experience and technical skills who have authority and responsibility
for testing and implementing research innovations in operational
systems.
3) Strong institutional incentives in both the research and operational
communities for sustained collaboration.
4) “Have fun at the pub.” Personal relationships and relationship building
between the research and operational communities over time are critical for
the success of the partnership.
Conclusions:
Human dimensions research on the capacity of existing
agencies and water managers to use climate forecasts
identified important research needs and fundamentally altered
the CIG’s strategy for education and outreach in the PNW.
Partnerships between RISAs, regional stakeholders and
operational agencies has been a very productive approach to
creating linkages between academia and management
agencies, and in the process of developing and refining pilot
climate forecast applications.
Ability to transfer forecasting technology from academia to
stakeholders and forecasting agencies remains a significant
barrier to the use of advanced forecasting systems in an
operational context. Inter RISA partnerships may play an
important role in solving these kinds of problems.