k thompson may 2014
DESCRIPTION
These are the slides from our May 23, 2014 Friday Forum workshop entitled 'Predicting and projecting the frequency of extreme marine events on time scales of days to decades with a focus on coastal flooding' led by Dalhousie University Professor Keith Thompson. The marine environment presents humankind with great economic opportunity but also major risks. It is a dangerous place to extract resources, and a particularly challenging environment for transportation, construction and human development. Our relationship with the marine environment is evolving due to climate change (e.g., global sea level rise, reduced pack ice in the Northwest Passage) and also shifts in economic and societal use (e.g., deep ocean drilling, marine recreational activities). In 2012 a new national network was established to bring together researchers and partners in a multi-sectoral partnership in order to improve Canada’s capabilities in Marine Environmental Observation, Prediction and Response (MEOPAR). In this talk Keith first provided an overview of this new network and then described some of its research, focusing mostly on coastal flooding. He then described how MEOPAR is making extended-range predictions of east coast storm surges, and the probability of coastal flooding, with lead times of hours to about 10 days. He also described a new statistically-based method for estimating the probability of coastal flooding over the next century, taking into account uncertainty in projections of sea level rise and storminess. Keith Thompson is a Professor at Dalhousie University with a joint appointment in the Department of Oceanography and the Department of Mathematics and Statistics. He holds a Canada Research Chair in Marine Prediction and Environmental Statistics. His research interests include ocean and shelf modelling, data assimilation, sea level variability, the analysis of extremes. New interests include the Madden Julian Oscillation and the Kuroshio Extension current system. He is presently a theme lead for the Marine Environmental Observation Prediction and Response (MEOPAR) network, a large national network established recently to help Canada respond more effectively to marine emergencies and change.TRANSCRIPT
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Keith Thompson Natacha Bernier Dalhousie University Environment Canada
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Overview of Talk
Overview of MEOPAR, a new naDonal network
PredicDng storm surges with lead Dmes up to 10 days
ProjecDng flood probabiliDes over coming decades
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MEOPAR in a Nutshell
New network of centers of excellence Marine Environmental ObservaDon PredicDon and Response Reducing vulnerability to marine hazards and emergencies Established in 2013, headquartered at Dalhousie $25M over 5 years from NCE program May be renewed twice Involves 50 researchers from 12 universiDes Partners include EC, DFO, DND, DRDC, Lloyds Register, ICLR, ...
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Dr. Harold Ritchie, Environment Canada/ Dalhousie University
A relocatable atmosphere-‐wave-‐ocean forecast system that can be set up within hours of a marine emergency.
Provide forecasts (hours to days) of physical properties of ocean and atmosphere to help guide response to an emergency. System to be transferred to Environment Canada for operational use.
A Relocatable Atmosphere-‐Ocean Prediction System Who:
What:
Impact:
Photo credit: ArcticNet
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Dr. Jinyu Sheng, Dalhousie Dr. Susan Allen, UBC
Build an integrated observation and prediction system for Halifax Harbour and Strait of Georgia.
Real-‐time forecasts of sea level, waves, currents, bio-‐geochemical properties for ports, municipalities, and the oil and gas sector.
Building Network of Fixed Coastal Observing & Forecast Systems
Who:
What:
Impact:
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Dr. Dany Dumont, UQAR
Improve surface drift forecasts in seasonally ice-‐infested seas. Some buoys deployed by the UQAR ice canoe team.
Respond to emergencies along Canadian coasts e.g., a person or oil patch. Time is key in ice-‐infested water.
Improving Surface Drift Forecasts
Who:
What:
Impact:
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Dr. Andrea Scott, University of Waterloo
Method to use radar (SAR) satellite images to improve the monitoring of sea ice.
Accurate information about sea ice conditions is critical for weather forecasting and safe navigation in ice-‐covered regions.
Improving Sea Ice Forecasts
Who:
What:
Impact:
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Dr. Gregory Flato, Environment Canada/ Uvic
Develop ways to assess and visualize changes in the marine environment and the associated risks on climate time scales.
The fishing industry and coastal communities could. e.g., use risk maps to manage their exposure to extreme weather events.
Climate Change and Extreme Events in the Marine Environment
Who:
What:
Impact:
Photo: CC Sam Beebe
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Dr. Katja Fennel, Dalhousie University
Develop biogeochmical, predictive models of the ocean and make climate projections.
Assist planning by, e.g., fishing industry, oil and gas industry, and coastal communities.
Biogeochemical Projections Under a Changing Climate
Who:
What:
Impact:
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Photo credit: ArcticNet
Dr. David Atkinson, UVic
Assess how large-‐scale weather patterns adversely impact marine transport and industrial activity in eastern Beaufort Sea.
Ensure marine operators, coastal communities and emergency response operators have access to weather forecast information to help plan operations.
User-‐Driven Monitoring of Adverse Marine and Weather States in the Eastern Beaufort Sea
Who:
What:
Impact:
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MEOPeople
Training highly qualified personnel is one of MEOPAR’s most important objectives.
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INFORMED SOCIETY
• More people using research results
• Information about the ocean readily available
COORDINATED CANADIAN APPROACH
• Bringing together researchers, industry, and NGOs
• Better techniques & policies
• Hazard management
TRAINED PEOPLE
• Ocean skills • Student mentoring
MEOPAR’S Outcomes
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PredicDng Storm Surges With Lead Times up to 10 Days
Storm surges are an ever present danger in eastern Canada
Home damaged by the storm surge of December, 2010 Sainte Luce, Quebec
hZp://joansullivanphotography.com/STILLS/Climate-‐change
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Flooding is Caused by Tide and Surge
€
η =ηT +ηS
Halifax February 1967
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ForecasDng Storm Surges
Surge models are usually based on two simple physical principles expressed by the following equaDons:
DiscreDze on a grid with realisDc coastlines and water depths. Integrate through Dme with forecast wind to forecast surge.
€
Du
Dt= − f ×u − g∇(η−ηp )+
τH−
cd u u
H∂η∂t
+∇ • (uH ) = 0
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Our Surge Model and Domain
• Model is 2D, based on POM
• Shelf and deep water, Labrador to Gulf of Maine
• Driven by 10 day forecast winds and air pressure
• DeterminisDc (1/30°)
• Ensemble (1/12°)
• 1 March 2013 to 31 March, 2014
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Typical DeterminisDc Forecasts
Rimouski ObservaDons in black
3 day forecasts
5 day forecasts
7 day forecasts
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How Good are the DeterminisDc Forecasts?
€
γ 2 =var(ηobs −ηmod )
var(ηobs )=
error
obs
For each of the 22 Dde gauges calculate
€
γ 2
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Allowing for Uncertainty in Wind Forecasts
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Visualizing Ensemble Surge Forecasts
5d forecast for 22 March 2013
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5 Day Forecasts of Total Water Level
€
η =ηT +ηS
Sea Level (m)
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ProjecDng Flood ProbabiliDes Over Coming Decades
Such informaDon is needed for sensible adaptaDon strategies.
Problem is conceptually similar to predicDng total water levels 10 days into future.
Let’s start by looking at some observaDons from the long Halifax sea level record.
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Annual Means and Maxima for Halifax
Halifax 1920-‐2001
Offset due to Ddes
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Annual Maxima About Annual Means
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Probability of Flooding Today
Halifax return level about mean (m)
+0.3m
Return period (years)
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100y ProjecDons of Flood ProbabiliDes
Simplest approach: Assume mean sea level will increase by fixed amount and just raise return levels. “DeterminisDc”.
But sea level increase over next century is highly uncertain (e.g., uncertain emission scenarios, model errors).
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Projected Sea Level Rise Over Next Century
IPCC, 2013: Summary for Policymakers. Figure SPM.9
“medium confidence”
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ProjecDng Probability of Total Water Level
Write annual maximum as sum of annual mean and a deviaDon:
Assume pdfs for these two components are of form:
The pdf of annual maximum is convoluDon of these two pdfs.
€
η = ηA + ηD
€
p(ηA ) = w1δ(ηA −ηS1)+ w2δ(ηA −ηS2 )+ ...
p(ηD ) = φG (ηD )
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Idealized Example Assume there are only possible SLR scenarios:
S1: Sea level rises at 0.3m per century P(S1)=0.8 S2: Sea level rises at 1.0m per century P(S2)=0.2
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Impact of Uncertainty on Return Levels
Return level for
Idealized Example
(m)
Return period (years)
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Average Dme between floods (years)
What Should Halifax Expect Today?
1.9m
300y
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Impact of 1.9m on Downtown Halifax
Charles et al., 2011
Expect one every 300y if present condiDons prevail
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Flood level (m)
What Should Halifax Expect in 2100?
1.9m
4y
Probability of exceeding high flood levels is determined by more extreme, but less likely, scenarios
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Trend toward probabilisDc predicDons and projecDons of sea level, based on ensembles and expert knowledge.
Uncertainty is not a sign of bad models or science.
Surge predicDons are improving (known unknowns). Expect rapid improvements over next five years.
Climate projecDons more complex (unknown unknowns?) BeZer understanding may lead to greater uncertainty.
Work presented here illustrates a small part of the research being conducted by MEOPAR.
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Impact of Uncertainty on Probability, and Number, of Floods with Time
Critical level Is 2 m