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BGCM

### Notes for the BGC modeling section

README.md

... | ... | @@ -114,4 +114,75 @@ Jean Chavy: |

114 | 114 | |

115 | 115 | Essi Aboyo: |

116 | 116 | - Dynamics of the NOW polynia |

117 | -- Idealized model for climate change | |

118 | 117 | \ No newline at end of file |

118 | +- Idealized model for climate change | |

119 | + | |

120 | +## BGCM | |

121 | + | |

122 | +Plankton: "drifters" organisms who drift with currents | |

123 | +Phytoplankton: Plankton that can perform photosynthesis using chlorophyll-a | |

124 | + | |

125 | +### Biological Pump | |

126 | +C02 + H20 -> Glucose + O2 | |

127 | + | |

128 | +Processes that pump atmospheric C2 into the ocean and eventually into the deep ocean | |

129 | + | |

130 | +Link between biology and climate | |

131 | +CO2 -> Phytoplankton -> Foodweb -> POC -> CO2 in the deep ocea | |

132 | +Physical pump: Through ventilation -> Northern Atlantic & Southern Ocean | |

133 | + | |

134 | +Model definition: schematic representation of processes following a reasoned approach | |

135 | + | |

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

137 | + | |

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

139 | + | |

140 | +Generally, need mixing depth < critical depth to have thriving phytoplankton. BUT, it will get nutrient depleted so you need deeper mixing some times. | |

141 | + | |

142 | +### Statistical model | |

143 | +Using: | |

144 | +- Degree days | |

145 | +- Southern Anomaly | |

146 | +- ENSO | |

147 | + | |

148 | +Best predictor: Degree days | |

149 | + | |

150 | +Logistics curve: dN/dt = rN(1-N/K) | |

151 | +r = b - m | |

152 | +b = birth | |

153 | +m = mortality | |

154 | + | |

155 | +Predator-Prey model | |

156 | +dN/dt = rN(1-N/K) - aNP/(C+N) | |

157 | +dP/dt = baNP/(c+N) - mP | |

158 | +1 - more prey | |

159 | +2 - more food/predator | |

160 | +3 - more predator | |

161 | +4 - more prey being eaten | |

162 | +5 - less prey | |

163 | +6 - less food/predator | |

164 | +7 - less predator | |

165 | +8 - less prey being eaten | |

166 | +9 - back to #1 | |

167 | + | |

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

169 | + | |

170 | +### Dany's part | |

171 | + | |

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

173 | + | |

174 | +First chose the boxes: Who's in your small pool | |

175 | +Then draw the arrows: How do they interract | |

176 | +Then pick a currency: Carbon, nitrate, chlorophyll, etc. Maybe more then one if you know the exchange rate | |

177 | +Figure out the fluxes and write down the equations | |

178 | + | |

179 | +NetLogo: simulate particles and define your own rules of interactions | |

180 | + | |

181 | +Chaos is | |

182 | +- deterministic | |

183 | +- nonlinear | |

184 | +- >= 3D | |

185 | +- sensitive to initial conditions | |

186 | + | |

187 | +Sochastic systems are not chaotic | |

188 | + | |

189 | +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) | |

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