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README.md

MEOPAR Winter School in Environmental Modeling Notes

Participant intros

Jenna Joyce: UoOttawa w/ Jackie Dawson

  • Shipping in the Arctic (Corridors)
  • Corridors & Environment/Culturally significant areas coexistence

Ewelina Luczko: Baird & associates "where land meets water"

  • Coastal engineering - wave modeling
  • Wave energy generation in SWAN

Lindsay Chipman: UoColorado - Boulder

  • Cycling of O2 and C in permeable sediment
  • O2 flux from

Lei Ren: NUI Galway

  • Surface flow fields from CODAR & Model
  • Focus area: West Coast of Ireland

Russel Glazer: UoFlorida - Tallahassee

  • Saturation over ice & water in models
  • Cloud formation scheme

Blanche St-Béat: ULaval - Qc

  • Food Webs: distinguish ecosystemséestablish stability
  • Resilience vs Resistance: Wood house is better than hay or bricks?!

Feifei Sun: Andrea Scott student

  • Predict ice thickness from data
  • New assimilation methods

Dennis Monteban: Denmark university

  • Study of Fjord west of Greenland / Wave-Ice damping
  • Validated & Calibrated MIKE model

Ben Moore-Maley: UBC w/ Susan Allen

  • 3D modeling of Salish Sea @ 500m resolution
  • Wind driven circulation/interactions

Nancy Chen: DFO - St-John's

  • Satellite SSH anomaly -> water transport
  • Labrador & Scotian Shelf

Deborah Benkort: PhD Laval

  • Krill aggregation & dispersion in GSL/GSE
  • Effect of envt. on growth, distribution, reproduction

Becky Segal: MSc UVic

  • Permafrost thaw sumps work before
  • Ice prediction/obs and creating relevant products for communities

Charles Brunette: McGill with Bruno

  • Predictability using Lagrangian methods
  • Seasonal & Regional
  • Later formation -> Thinner ice -> Minimum ice extent

Onur Bora: Coastal engineer/PhD student in Istanbul

  • Hydrodynamics effects from shipping -> impact on sediment
  • Water cooling/intake system design

Olivier Asselin:

  • Figure out the atmosphere
  • Energy constant vs \lambda for different processes: 2 slopes for everything?!
  • CFD

Nathan Grivault: UoAlberta w/ Paul Myers

  • Freshwater in the Arctic: Arctic -> Lower latitudes
  • Impact on circulation: Export vs Forcing

UQAR People

Claudia Carascal:

  • Satellite validation of reflectance
  • Adjust for atmosphere/ocean to get buoy value

Jean-Luc Shaw: w/ Daniel and Dany

  • Hydrodynamics of the Bay of Sept-Iles
  • Numerical model & validation to support ecological indicators

Eliott Bismuth: w/ Dany

  • Wave-ice interactions & shore protection
  • Now erosion as an RA
  • Wave data/modeling/validation + XBeach

Sandy Gregorio: w/ Zhigang Xu (DFO)

  • Modeling storm surge & tsunami
  • Atmosphere: frequency of storms from low P events
  • ocean to get impact

Gwenaëlle Gremion: w/ Dany

  • Biological carbon pump in NOW polynia
  • Understand/represent carbon flux under water (and increased C at depth?)
  • BGCModel + PO to assess Bio + Physics effects

Jeremy Beaudry: w/ Dany

  • Wave attenuation / ice breakup
  • 1D floe size & thickness distribution for 2D WW3

Manu John:

  • Wave-Ice interactions for regional modeling
  • HF radar

Sebastien Dugas:

  • Wave-floe size/concentration relationship

Michel Tantare:

  • Drift prediction/comparison
  • Eullarian vs Lagrangian methods for drift?

Jean Chavy:

  • Energy transfer/KE cascade
  • Combine radar wind obs to get winds

Essi Aboyo:

  • Dynamics of the NOW polynia
  • Idealized model for climate change

BGCM

Plankton: "drifters" organisms who drift with currents Phytoplankton: Plankton that can perform photosynthesis using chlorophyll-a

Biological Pump

C02 + H20 -> Glucose + O2

Processes that pump atmospheric C2 into the ocean and eventually into the deep ocean

Link between biology and climate CO2 -> Phytoplankton -> Foodweb -> POC -> CO2 in the deep ocea Physical pump: Through ventilation -> Northern Atlantic & Southern Ocean

Model definition: schematic representation of processes following a reasoned approach

Given a selection of data, pick your model? Based on understanding? (Akaike) Information Criterion?

Sverdrup Critical Depth: Depth at which phytoplankton losses overcome gains. You should only find (happy) phytoplankton above.

Generally, need mixing depth

Statistical model

Using:

  • Degree days
  • Southern Anomaly
  • ENSO

Best predictor: Degree days

Logistics curve: dN/dt = rN(1-N/K) r = b - m b = birth m = mortality

Predator-Prey model dN/dt = rN(1-N/K) - aNP/(C+N) dP/dt = baNP/(c+N) - mP 1 - more prey 2 - more food/predator 3 - more predator 4 - more prey being eaten 5 - less prey 6 - less food/predator 7 - less predator 8 - less prey being eaten 9 - back to #1

The keep building more complicated things and instead of considering that they're all together (0D), put them in a 1-2-3D model!

Dany's part

There is rarely a model ready to fit all you need.

First chose the boxes: Who's in your small pool Then draw the arrows: How do they interract Then pick a currency: Carbon, nitrate, chlorophyll, etc. Maybe more then one if you know the exchange rate Figure out the fluxes and write down the equations

NetLogo: simulate particles and define your own rules of interactions

Chaos is

  • deterministic
  • nonlinear
  • >= 3D
  • sensitive to initial conditions

Sochastic systems are not chaotic

Ergodicity: Long average in space is equivalent to long average in time. Ex.: Waves (you can average sea level over a transect of length L, or you equivalently could sit still and average what you encounter over a certain time period equal to T = L/c_p)