Designing for Global Warming Orson P. Smith, PE, Ph.D. School of Engineering.

Designing for Global Designing for Global WarmingWarming

Orson P. Smith, PE, Ph.D.

School of Engineering

Orson Smith - UAA School of Engineering

10 February 2004 Alaska Forum on the Environment

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Evidence of global warming continues to accumulate

Combined global annual land-surface air and sea surface temperature anomalies



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Strongest signals are in the North



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Projections are Scattered

Source: Intergovernmental Panel on Climate Change, 2001



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Global Circulation Model (GCM) Predictions Vary

Average, minimum, and maximum air temperatures predicted from 27 GCM’s for Fairbanks, Alaska (with permission from Vinson and Bae, 2002, “Probabilistic Analysis of Thaw Penetration in Fairbanks, Alaska,” ASCE Cold Regions Engineering Conference, Anchorage)



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Figure from EPA website http://www.epa.gov/globalwarming/

Other Trends Complicate Predictions



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Climate change impacts involve spatial variables

Permafrost changes

• Thaw subsidence, onshore and offshore

• increased flux of sediments into steams and the coastal ocean

Permafrost

Chf

Cmf

Clf

Dhf

Dmf

Dlf

Dhr

Dlr

Shf

Smf

Slf

Shr

Slr

Ihf

Imf

Ilf

Ihr

Ilr

Chr

Clr

glacier

ocean/Inland seas

land

Subsea

sea-ice edge limit

subsea permafrost l imit

treeline

Continuous(90 - 100%)Continuous(90 - 100%)Continuous(90 - 100%)

Discontinuous(50 - 90%)

Sporadic(10 - 50%)

IsolatedPatches(0 - 10%)

Extract of Circum-Arctic Map of Permafrostand

Ground Ice Conditions1997

Source DataU.S. Geological Survey

International Permafrost Association

500 0 500

Kilometers



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Alaska’s Permafrost Foundations are at Risk

500 0 500

Kilometers#

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Source DataU.S. Geological Survey

International Permafrost Associationand

Alaska Department of Natural Resources GIS Database

Alaska Community and Highway Permafrost Exposure

Permafrost Extent Road Distance (km)

Continuous (90 - 100%) 734

Discontinuous (50 - 90%) 1950

Sporadic (10 - 50%) 307

Less than 10% 452

Summary of Alaska Highways Susceptible to Permafrost

Permafrost Extent Total Communities Population

Continuous (90 - 100%) 87 40811Discontinuous (50 - 90%) 79 47140Sporadic (10 - 50%) 26 5235Less than 10% 129 396821

Summary of Alaska Communities Susceptible to Permafrost

Cartographic Illustration:Wm. J. Lee

Suseptible Roads

Continuous (90 - 100%)

Discontinuous (50 - 90%)

Sporadic (10 - 50%)

Less than 10%

Suseptible Communities

Continuous (90 - 100%)#

Discontinuous ( 50 - 90%)#

Less than 10%#

Sporadic ( 10 - 50%)#

GEOMATICS



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•Proven responses to most warming problems exist

•Accurate knowledge of change saves money

•Synthesize existing data

•Monitor changes statewide

•Improve data transfer

•Refine predictions

•Revise codes, manuals, and design software

Engineers' Viewsfrom Prior Meetings



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Strategies for Climate Change Design Criteria Development

Designers may address climate change by:

• Subjective: factor of safety

• Deterministic: apply a trend

• Probabilistic: Monte Carlo simulations

• Hybrid: e.g., apply “fuzzy set” methods



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Monte Carlo Simulations

•Random sampling, interpreted by assumed continuous distributions of independent variables

•Many repetitions results in a derived distribution of dependent variable



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Apply a Trend

• Designers focus only on extremes

• Trends apply to entire data set

• Accelerated change is not resolved by conventional criteria development methods– Additional information is necessary

• More sophisticated historical data analysis

• Predictive simulations (GCM results, Monte Carlo, …)



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Accelerated Trend

Storm-related extreme conditions may have accelerated trends from more frequent and intense storms due to global warming



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Projections from history

Consider the first half of the previous time series as a hypothetical historical record

Threshold of extremes



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Conventional Extremal Analysis

)(

)(

x

eexF

Return period:

)(1

1

xFTR

Cumulative Probability:

Extrapolated 50- & 100-year return period values

8.694928.43899



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Anticipate a Linear Trend

bmxy



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Anticipate a Linear Trend

Remove the trend and identify extremes

Fit extremal distribution



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Trend-adjusted Extrapolations

bTmyy RTadj R



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Anticipate an Accelerating Exponential Trend

caey bx



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Exponential Trend-adjusted Extreme Values

100-year return period value

50-year return period valuecaeyy R

R

bTTadj

)(

)(

x

eexF



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Summary of Proposed Analysis

1. Derive trend from complete data set

2. Remove trend from data set

3. Apply conventional statistics of extremes

4. Adjust extrapolated extremes with trend



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Cycle Superimposed on an Exponential Trend



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Options for Addressing Climate Cycles

• Remove cycles with low pass filter (10 - 20 year period)

• Ignore cycles

Decades of good data are required to define a regional climate cycle



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Questions

1. What are the fundamental trends, cycles, and distributions of engineering parameters?

2. How do we best anticipate a trend in forecasting secondary variables (floods, storm surge, erosion, thaw depth, etc.)?

3. How do we best anticipate a trend for design criteria development (extremal analysis)?

4. How do we best anticipate a cycle for design criteria development?

Designing for Global Warming Orson P. Smith, PE, Ph.D. School of Engineering.

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