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  Keith	
  Thompson	
  	
   	
  	
   	
  Natacha	
  Bernier	
  
Dalhousie	
  University	
   	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  Environment	
  Canada	
  
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	
  
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,	
  ...	
  	
  
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	
  
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:	
  
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:	
  
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:	
  
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	
  
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:	
  
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:	
  
MEOPeople	
  
Training	
  highly	
  qualified	
  personnel	
  is	
  one	
  of	
  
MEOPAR’s	
  most	
  important	
  objectives.	
  
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	
  
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	
  
Flooding	
  is	
  Caused	
  by	
  Tide	
  and	
  Surge	
  
€
η =ηT +ηS
Halifax	
  
February	
  1967	
  
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
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	
  
Typical	
  
DeterminisDc	
  	
  
Forecasts	
  
	
  Rimouski	
  
	
  ObservaDons	
  in	
  black	
  
3	
  day	
  forecasts	
  
5	
  day	
  forecasts	
  
7	
  day	
  forecasts	
  
How	
  Good	
  are	
  the	
  DeterminisDc	
  Forecasts?	
  
€
γ2
=
var(ηobs −ηmod )
var(ηobs )
=
error
obs
For	
  each	
  of	
  the	
  22	
  Dde	
  gauges	
  calculate	
  
γ2
Allowing	
  for	
  Uncertainty	
  in	
  Wind	
  Forecasts	
  
Visualizing	
  Ensemble	
  Surge	
  Forecasts	
  
5d	
  forecast	
  for	
  
22	
  March	
  2013	
  
5	
  Day	
  Forecasts	
  of	
  Total	
  Water	
  Level	
  
€
η =ηT +ηS
Sea	
  
Level	
  
(m)	
  
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.	
  
Annual	
  Means	
  and	
  Maxima	
  for	
  Halifax	
  
Halifax	
  
1920-­‐2001	
  
Offset	
  due	
  
to	
  Ddes	
  
Annual	
  Maxima	
  About	
  Annual	
  Means	
  
Probability	
  of	
  Flooding	
  Today	
  
Halifax	
  
return	
  level	
  
about	
  mean	
  
(m)	
  
+0.3m	
  
Return	
  period	
  (years)	
  
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).	
  
Projected	
  Sea	
  Level	
  Rise	
  Over	
  Next	
  Century	
  	
  
IPCC,	
  2013:	
  	
  
Summary	
  for	
  
Policymakers.	
  	
  
Figure	
  SPM.9	
  
“medium	
  
confidence”	
  
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 )
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	
  	
  
Impact	
  of	
  Uncertainty	
  on	
  Return	
  Levels	
  
Return	
  level	
  
for	
  
Idealized	
  
Example	
  
(m)	
  
Return	
  period	
  (years)	
  
Average	
  Dme	
  between	
  floods	
  (years)	
  
What	
  Should	
  Halifax	
  Expect	
  Today?	
  
1.9m	
  
300y	
  
Impact	
  of	
  1.9m	
  on	
  Downtown	
  Halifax	
  
Charles	
  et	
  al.,	
  2011	
  
Expect	
  one	
  every	
  
300y	
  if	
  present	
  
condiDons	
  prevail	
  
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	
  
  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.	
  	
  	
  
Impact	
  of	
  Uncertainty	
  on	
  Probability,	
  	
  
and	
  Number,	
  of	
  Floods	
  with	
  Time	
  
Critical	
  level	
  
Is	
  2	
  m	
  

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K thompson may 2014

  • 1.    Keith  Thompson          Natacha  Bernier   Dalhousie  University                      Environment  Canada  
  • 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. 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.
  • 5. 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  
  • 6. 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:  
  • 7. 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:  
  • 8. 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:  
  • 9. 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  
  • 10. 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:  
  • 11. 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:  
  • 12. MEOPeople   Training  highly  qualified  personnel  is  one  of   MEOPAR’s  most  important  objectives.  
  • 13. 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  
  • 14. 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  
  • 15. Flooding  is  Caused  by  Tide  and  Surge   € η =ηT +ηS Halifax   February  1967  
  • 16. 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
  • 17. 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  
  • 18. Typical   DeterminisDc     Forecasts    Rimouski    ObservaDons  in  black   3  day  forecasts   5  day  forecasts   7  day  forecasts  
  • 19. How  Good  are  the  DeterminisDc  Forecasts?   € γ2 = var(ηobs −ηmod ) var(ηobs ) = error obs For  each  of  the  22  Dde  gauges  calculate   γ2
  • 20. Allowing  for  Uncertainty  in  Wind  Forecasts  
  • 21. Visualizing  Ensemble  Surge  Forecasts   5d  forecast  for   22  March  2013  
  • 22. 5  Day  Forecasts  of  Total  Water  Level   € η =ηT +ηS Sea   Level   (m)  
  • 23. 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.  
  • 24. Annual  Means  and  Maxima  for  Halifax   Halifax   1920-­‐2001   Offset  due   to  Ddes  
  • 25. Annual  Maxima  About  Annual  Means  
  • 26. Probability  of  Flooding  Today   Halifax   return  level   about  mean   (m)   +0.3m   Return  period  (years)  
  • 27. 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).  
  • 28. Projected  Sea  Level  Rise  Over  Next  Century     IPCC,  2013:     Summary  for   Policymakers.     Figure  SPM.9   “medium   confidence”  
  • 29. 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 )
  • 30. 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    
  • 31. Impact  of  Uncertainty  on  Return  Levels   Return  level   for   Idealized   Example   (m)   Return  period  (years)  
  • 32. Average  Dme  between  floods  (years)   What  Should  Halifax  Expect  Today?   1.9m   300y  
  • 33. Impact  of  1.9m  on  Downtown  Halifax   Charles  et  al.,  2011   Expect  one  every   300y  if  present   condiDons  prevail  
  • 34. 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  
  • 35.   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.      
  • 36. Impact  of  Uncertainty  on  Probability,     and  Number,  of  Floods  with  Time   Critical  level   Is  2  m