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Uncertainty in Climate Extremes: The Science, Impacts, and Policy Relevance

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Title: Uncertainty in Climate Extremes: The Science, Impacts, and Policy Relevance


1
Uncertainty in Climate Extremes The Science,
Impacts, and Policy Relevance
Presentation Venue Opportunities Challenges in
Uncertainty Quantification for Complex
Interacting Systems Workshop Sponsored by NSF
and NNSA Auroop R. Ganguly Oak Ridge National
Laboratory gangulyar_at_ornl.gov 12th 14th April
2009 University of Southern California Los
Angeles, CA
Alumni Shiraj Khan Gabriel Kuhn Chris Fuller
Major Contributors Karsten Steinhaeuser Shih-Chi
eh Kao Esther Parish
Visit our website http//www.ornl.gov/knowledgedi
scovery/ClimateExtremes
2
Knowledge Discovery System for Climate Extremes
and Uncertainty
3
Extreme Hydro-Meteorological Stresses
Caused or exacerbated by climate change
4
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5
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6
Extreme Hydro-Meteorological Events
Caused or exacerbated by climate change
IPCC AR4, 2007
7
Regional Extreme Events
8
Regional Extreme Events
9
Regional Extreme Impacts
10
Precipitation Return Levels
  • T-year Return Level, RL(T)
  • Level that will be exceeded once in every T years
  • Probability of exceeding RL(T) in any year 1/T

N-year Return Level u Threshold exceeded in a
year ny Observations in a year zu Probability
of an individual observation exceeding u
11
Trends in Precipitation Return Levels
50 year RL
R2
Observed
Trends
200 year RL
12
Precipitation Extremes Volatility Index
RL(200) RL(50)
Trends
PEVI
Observed
13
Geospatial-Temporal Risk Indices
14
Extremes Goodness of (POT) Theory
1940-2004
1965-1989
1980-2004
Goodness of Fit Statistic for Peak-Over-Threshold
Extreme Value Theory Note Values lt 1 ? Cannot
reject inter-arrival times follow homogeneous
Poisson Observed daily precipitation (above) and
weekly maxima residuals (below)
15
Extremes Uncertainty in Estimation
Spatial Trends Uncertainty
Shape Parameter
Standard Errors
1940-2004 Observed Weekly Maxima Residuals
Temporal Trends Uncertainty
1965 2004 Linear Trend
1965 2004 Linear R2
16
Model vs. Observed Precipitation Correlation
Tail Dependence in Rainfall
Observed
CCSM3
17
Model vs. Observed Precipitation Pair-wise
Correlation Tail Dependence
Observed
Correlation
Tail Dependence
CCSM3
18
Predictive Skills of Climate Models
19
Projections Uncertainty A1FI Temperature
20
Projections Uncertainty A1FI Heat Waves
21
Uncertainty 2050 A1FI Precipitation
CCSM3 Model
22
Understanding Uncertainty in Precipitation
23
Uncertainty in Precipitation Extremes
No statistical distribution for extremes emerges
as a clear winner
Block maxima based on GEV projects 100-year
return levels for monthly precipitation becoming
less than 10-year return levels by 2100…
24
Standard Index for Droughts Trends
Reanalysis shows a clear upward trend in the
drought index
No clear trend observed in model (CCSM3 A1FI)
25
SI for Regional / Decadal Droughts
Regional 30-yr. average trends in drought
indices, computed from A1FI hindcast parameters,
appear to intensify in the latter half of the
21st century
26
Cascading Uncertainty Precipitation (A1FI-A2)


27
Uncertainty Reduction Correlative Analysis
Statistically higher correlation in ENSO vs.
Tropical River Flows
Mutual information based robust nonlinear
correlation
28
Uncertainty Reduction Predictive Insights
Simple correlations (left) confirm existing
insights
Complex networks (bottom) promise novel insights
Space-time community detection (left) uses
correlative analysis and offers new tool in
climate
29
A Climate Change War Game Description
  • A role playing exercise
  • Four largest emitters discuss climate policy
  • UN Secretary General presses for emission target
  • Players balance national interests and global goal
  • The Players
  • Diverse backgrounds
  • Climate scientists
  • Security strategists
  • Environmental policy experts
  • Business leaders
  • Forty national delegates
  • United States
  • European Union
  • China
  • India
  • UN Team
  • Center for a New American Security
  • John Podesta UN Secretary General
  • UN Sec. Gen.s Science Team
  • Rest of the world (observer status)
  • Coverage
  • The journal Nature (Blogs, News)
  • ABC News Documentary
  • Videocon by IPCC chairman Pachauri
  • Prominent players from US, EU, Asia

John Podesta (left) of CNAS, the UN secretary
general in the game, was Clintons White House
chief of staff and led Obamas transition team
30
UN Secretary General charges the country
delegates with four tasks
  • The Four Negotiation Areas
  • Natural (e.g., water) resources scarcity
  • Hazards and humanitarian aid
  • Migration and population shifts
  • Reduction of greenhouse gas emissions
  • Adaptation Mitigation
  • Adaptation Manage the Unavoidable
  • Mitigation Avoid the Unmanageable

CNAS Website for Climate Change War
Game http//www.cnas.org/node/149
31
Global Assessments The 'Angry Red Chart'
Nature Blogs
Courtesy Nature Blog (Tollefson) CNAS (John
Podesta) Pew Center (Gulledge) CNAS (Sharon Burke)
32
A Climate Change War Game Publicity
Much as the "hockey stick graph" became an icon
for global warming itself, the "Angry Red Chart"
became a symbol of the science that was driving
negotiations back in the year 2015 (Jeff
Tollefson, Nature Blogs)
Nature Blogs http//blogs.nature.com/news/blog/ev
ents/climate_war_game/
33
Regional Consequences Hydrology (1)
Projected Difference between end and beginning of
21st century JJA (Summer)
34
Regional Consequences Hydrology (2)
Projected Difference between end and beginning of
21st century JJA (Summer)
35
Regional Consequences Hydrology (3)
Projected Difference between end and beginning of
21st century JJA (Summer)
36
Translating Science to Policy
  • The angry red chart visual motivated much of
    the global consensus around emissions
  • Example The policy-makers from all four regions
    tried desperately to agree on some common
    language to reduce emissions
  • Adaptation decisions and policies were based on
    hard realities on the ground
  • Example Advanced US capabilities for disaster
    management around the globe were a primary
    consideration that dominated the discussions
    around hazards and humanitarian aid
  • Clear depiction of regional extreme stresses or
    events, including uncertainty, motivated
    bilateral and trilateral agreements, leading to
    global consensus
  • Example The India team utilized regional maps of
    hydrologic / water extremes and stresses to
    motivate delegates from the US team to extract
    international norms regarding water treaties, in
    spite of some resistance from the China team

37
Lessons Learnt
  • Relevance of the science
  • Global assessments useful for international
    policy
  • Regional assessments useful for adaptation
    treaties
  • Climate extremes are among the most relevant
  • Importance of uncertainty
  • Uncertainty information is useful and actionable
  • Uncertainty cannot be an excuse for inaction
  • Quantification of known uncertainty translates to
    risks
  • Comprehensive uncertainty characterization is a
    key
  • Uncertainty reduction is a research priority

38
The Unquantifiable Uncertainties
The consequence of possible de-glaciation of
Himalayan glaciers in the Indian sub-continent
and China are hard to quantify but harder to
ignore for policy decisions…
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