Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

K thompson may 2014

441 vues

Publié le

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.

Publié dans : Sciences, Technologie, Business
  • DOWNLOAD FULL. BOOKS INTO AVAILABLE FORMAT ......................................................................................................................... ......................................................................................................................... 1.DOWNLOAD FULL. PDF EBOOK here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... 1.DOWNLOAD FULL. EPUB Ebook here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... 1.DOWNLOAD FULL. doc Ebook here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... 1.DOWNLOAD FULL. PDF EBOOK here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... 1.DOWNLOAD FULL. EPUB Ebook here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... 1.DOWNLOAD FULL. doc Ebook here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... ......................................................................................................................... ......................................................................................................................... .............. Browse by Genre Available eBooks ......................................................................................................................... Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, Cookbooks, Crime, Ebooks, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult,
       Répondre 
    Voulez-vous vraiment ?  Oui  Non
    Votre message apparaîtra ici
  • Soyez le premier à aimer ceci

K thompson may 2014

  1. 1.    Keith  Thompson          Natacha  Bernier   Dalhousie  University                      Environment  Canada  
  2. 2. 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  
  3. 3. 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,  ...    
  4. 4. 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  
  5. 5. 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:  
  6. 6. 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:  
  7. 7. 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:  
  8. 8. 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  
  9. 9. 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:  
  10. 10. 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:  
  11. 11. MEOPeople   Training  highly  qualified  personnel  is  one  of   MEOPAR’s  most  important  objectives.  
  12. 12. 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  
  13. 13. 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  
  14. 14. Flooding  is  Caused  by  Tide  and  Surge   € η =ηT +ηS Halifax   February  1967  
  15. 15. 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 )+ τ H − cd u u H ∂η ∂t + ∇ • (uH ) = 0
  16. 16. 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  
  17. 17. Typical   DeterminisDc     Forecasts    Rimouski    ObservaDons  in  black   3  day  forecasts   5  day  forecasts   7  day  forecasts  
  18. 18. How  Good  are  the  DeterminisDc  Forecasts?   € γ2 = var(ηobs −ηmod ) var(ηobs ) = error obs For  each  of  the  22  Dde  gauges  calculate   γ2
  19. 19. Allowing  for  Uncertainty  in  Wind  Forecasts  
  20. 20. Visualizing  Ensemble  Surge  Forecasts   5d  forecast  for   22  March  2013  
  21. 21. 5  Day  Forecasts  of  Total  Water  Level   € η =ηT +ηS Sea   Level   (m)  
  22. 22. 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.  
  23. 23. Annual  Means  and  Maxima  for  Halifax   Halifax   1920-­‐2001   Offset  due   to  Ddes  
  24. 24. Annual  Maxima  About  Annual  Means  
  25. 25. Probability  of  Flooding  Today   Halifax   return  level   about  mean   (m)   +0.3m   Return  period  (years)  
  26. 26. 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).  
  27. 27. Projected  Sea  Level  Rise  Over  Next  Century     IPCC,  2013:     Summary  for   Policymakers.     Figure  SPM.9   “medium   confidence”  
  28. 28. 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 )
  29. 29. 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    
  30. 30. Impact  of  Uncertainty  on  Return  Levels   Return  level   for   Idealized   Example   (m)   Return  period  (years)  
  31. 31. Average  Dme  between  floods  (years)   What  Should  Halifax  Expect  Today?   1.9m   300y  
  32. 32. Impact  of  1.9m  on  Downtown  Halifax   Charles  et  al.,  2011   Expect  one  every   300y  if  present   condiDons  prevail  
  33. 33. 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  
  34. 34.   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.      
  35. 35. Impact  of  Uncertainty  on  Probability,     and  Number,  of  Floods  with  Time   Critical  level   Is  2  m  

×