Download Pitfalls, Uncertainty, and Opportunities in Climate Change Science

Pitfalls, Uncertainty, and Opportunities
in Climate Change Science:
NASA’s System Engineering for Decision Support
University of Nebraska
October 24-26, 2007
U.S. Geological Survey
L. DeWayne Cecil
Chief for Science Applications
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System Diagram for NASA’s Applied Sciences
Activities
Research
and Analysis
Program
Applied Sciences
Program
Crosscutting
Solutions
supply
Operations
National
Applications
demand
R&O
NASA
Earth Science
Research
Solutions
Network
Rapid Prototyping
Capacity
Integrated
System Solution
Societal
Benefits
•Water and Energy
• Verification
•Benchmarking
•Climate
•Evaluation
and
•Weather
Validation
•Carbon
Uncertainty Analysis, Scientific Rigor, Community Peer Review
•Solid Earth
•Atmospheric Composition
•Solar
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Research to Operations and
Decision Support;
Advantage Operations!
•Department of Interior Advantage; Development of
Decision Support Systems and Tools by Charge
•Perhaps we need a DOI Climate Change Science Central,
Analogous to Commerce's NCDC, Ashville, NC
• Alternative Approaches
•Larger Portion of R&A Portfolio becomes Applications
•UC-Boulder is Good Example
•NOAA’s RISA Program
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Bringing Global Climate Change Model Projections to
the Watershed Scale: Pitfalls, Opportunities, and
Uncertainties for Decision Support
EARTH SYSTEM MODELS
AND DATASETS
• Watershed scale, 2-D, snow, ice, and
water mass balance model (Plummer
and Phillips, 2005) with input from
NASA global scale projections
•Rigorous large ensemble
probability distribution analyses
• OSSE datasets for next generation
satellites
•Rigorous statistical design built
into OSSEs upfront (NIST as a
partner)
• Climate Models in ESMF: GISS Model E
and other GMAO Analyses
RAPID PROTOTYPING V&V
DECISION SUPPORT TOOLS
•Uncertainty analyses,
uncertainty analyses,
Uncertainty analyses!
•Global/Regional/Watershed scale model
products
• Regional differences
in aerosols and trace
gas concentrations and
impacts on climatology
•12 – 18 month
seasonal forecasts,
5 – 20 year projections,
Data
EARTH OBSERVATIONS
• Atmosphere: Aura, TRMM,
OCO, CALIPSO, CloudSat,
GPM, Aquarius
• Land : ICESat, MODIS
• Field Mission: Watershedscale airborne campaigns,
Ground-based monitoring
network
*Next Generation Missions
Potential
Partners
& Century timescale
projections
• Natural & anthropogenic
aerosols, black carbon
• Trace gas profiles
• Climate-Change
Parameters
• Tropical/Global/Regional
Precipitation
• Total Aerosols
• Use OSSE simulated
next generation and
current mission
datasets for climate
change scenario
assessments WITH
associated
uncertainties carried
throughout projections
• Use OSSE simulated
data from next
generation missions
with existing
measurements of
climate change
parameters from space
to estimate the
watershed-scale mass
balance and climate
change impacts
VALUE & BENEFITS
•Impacts of global
climate change on
the Watershed scale
•Water resource
management on
local scale
•Climate-change
impacts on wastemanagement facility
sighting
•Decision support
with uncertainty
quantified and
communicated (NSF
as a partner)
• Interagency Alignment:
CCSP, CCTP, US GEO
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Uncertainty Analysis, Scientific Rigor, Community Peer Review
Bringing Global Climate Change Model Projections
to the Watershed Scale: Pitfalls, Opportunities, and
Uncertainties for Decision Support
• Watershed-Scale Applied Science Questions?
(1) How can global predictions of the effects of
future rapid climate change and variability
be enhanced and used?
(2) How are uncertainties in global projections
compounded, or not, at the watershed scale?
(3) What are the implications for decisionsupport?
Øksfjordjøkelen, Norway
Upper Fremont Glacier, Wyoming, USA
Beluka Glacier, Siberia, Russia
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Tools to Address the Science Questions
•Datasets Generated From Future-Mission OSSEs
•OCO, CALIPSO, CloudSat, GPM, Aquarius
•Datasets From Current Missions
•Aura, TRMM, ICESat, MODIS
•Global Climate Change Model Projections Using All Data
•GISS E, other GMAO Analyses
•Global Model Output Used As Watershed-Scale Model Input
•ESMF Compliant NASA Model Codes
•Partner’s ESMF Compliant Model Codes
•Uncertainty Analyses of Inputs and Outputs
Decision Support
Inputs/Outputs MAP 2006
OSSE Datasets
Tools for
Uncertainty Analysis
Inputs/Outputs MAP 2005
GEOS-5
Watershed
Scale MB
MODEL
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JCSDA
fvGCM (GEOS4)
Project Columbia
Example Statistical Tools with Potential to
Enhance OSSE Analysis/Interpretation and For
Analyzing Uncertainty of Model Projections
Statistical Analyses Built Into Experiments Up Front
• Allow efficient, unambiguous comparisons of
different methods for using data, including
interactions between factors
Uncertainty analysis
• Propagation of uncertainty in model inputs and
outputs
• Necessary for realistic interpretation of all types of
measurement results
• Computer intensive methods such as the Bootstrap
and Bayesian analyses using Markov Chain Monte
Carlo have the potential to handle complicated,
intricate model projections and datasets
• Augment rigorous large ensemble probability
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distribution determinations
Visualization of Experimental Results
for Decision Support
WHERE DID WE GO WRONG?
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2-D Visualization of The Effects of a 5o Celsius
Air-Temperature Suppression over 300 Years
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Why do We Care?
What Can the Past Tell US?
Late Pleistocene
pluvial lakes in the
Western Great Basin
Reheis et al., 1989
Yucca Mtn., Nevada
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What are the Implications for
Decision Support?
Do we site our Nation’s mixed high-level
radioactive and chemical waste repository
at this location?
If so, how do we include the climate-change
scenario assessment in our decision making
and in the design of our repository?
Next Set of Questions for Decision Support
How certain are we of the effects of the climate-change
scenarios from global to watershed scale?
What are the associated uncertainties, how are they
communicated to the decision and policy makers, and how
do they influence our decisions?
What are the metrics for determining if we have provided the
science data products from NASA’s current and next
generation missions to enhance the decision making?
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Metrics for Determining Successes and
Shortcomings of an Evolving Process
Example Integrated System Solutions Metrics
**Do Not Rely On One Metric Alone**
•Quantification and Communication of Uncertainty
•In Datasets
•In Climate-Change Projections (Forcings)
•In Decision Making
•Transparent and Inclusive Community of Practice
Peer Review (This implies publication of results!)
•“Improved” Decision Support
•Expert analysts’ surveys are a significant first
step, we must continue with more rigorous
metrics, i.e., how did we enhance decision
support, which combinatory factors/science
products maximized enhancement?
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Sources of Uncertainty in Earth-system
Science
• Uncertainty in Conceptual Model of System
•Watershed Scale Project
•Knowledge of Terrain Effects
•Changing Source of Storms and Precipitation
•Local-Regional-Global Drivers
•Uncertainty in Tools to Test Conceptual Model
•Observational Datasets
•Satellite CDRs (Climate Data Records), Long-Term, Traceable,
Established Cal/Val, i.e. TSI, 30 year record
•In-situ Monitoring Network, Generally Short-Term, Cal/Val
Is Underway
•Digital Models
•Incorrect concepts used to construct model code
•Who is checking this? Other Modelers!
•Suggest Independent Review Panels of Interdisciplinary
Users, Decision Makers, and Data Providers as well as
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Modelers
Sources of Uncertainty in Earth-system
Science (continued)
• Applications
•Wrong Data for Input, Wrong Model Selected, Wrong
Application of Model
Scale is Wrong
Temporal Division is Wrong
Process (Weather to Climate) is Wrong
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Potential Partners to DOI and Sources of Experienced
Researchers in Quantifying and Communicating Uncertainty
for Decsion Support
Potential
Partners
NIST
• Statistical Engineering Group
• Bootstrap Program, good for virtual datasets
• Bayesian Methods
• Monte Carlo Techniques
National Science Foundation’s Decision Making Under
Uncertainty in Climate Science (year 2+ of 5 year program)
• Climate Decision Center, Carnegie-Mellon University
• Decision Center for a Desert City, Arizona State U.
• Columbia University
• UC-Boulder
• RAND
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Contact Information
L. DeWayne Cecil, Ph.D.
USGS
900 N. Skyline Drive, Suite C
Idaho Falls, ID 83402
208-528-2611
[email protected]
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