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Lagrangian-based studies in the coastal Gulf of Maine

This study aims to estimate community production rates in the Gulf of Maine via Lagrangian-based approaches. Using multiple views from GeoCAPE, the research tracks biogeochemical inventories over time. Part 1 involves a Lagrangian field experiment to determine net community productivity, while Part 2 analyzes preliminary results of tracking particle inventories with a circulation model and MODIS data. Results show significant relationships between inventories and net community production, leading to insights for the Gulf of Maine's biogeochemistry.

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Lagrangian-based studies in the coastal Gulf of Maine

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  1. Lagrangian-based studies in the coastal Gulf of Maine Overall goal: estimation of community production rates by tracking satellite-derived inventories over time and space Problem: LEO-derived productivity estimates presently rely on single images of stocks and state variables to infer rates of change Solution: Multiple views/day from GeoCAPE will enable monitoring of biogeochemical inventories within a water parcel as they evolve over time.

  2. Lagrangian-based Studies in the coastal Gulf of Maine (supplements to NASA-Carbon NNX08AL80G). Part 1: A Lagrangian field experiment to determine net community productivity in the Gulf of Maine Olivia DeMeo (MS Candidate, UNH), Joe Salisbury (UNH) Part 2: Preliminary results of tracking particle inventories with a high resolution circulation model and MODIS 250m data Bror Jonsson (Princeton), Joe Salisbury (UNH), Amala Mahadevan, (Boston University)

  3. Part 1 The Lagrangian Experiment Approach: 1. Tracked a drogue at 12m (7 cruises over 16 days) 2. Kept track of oxygen and particle inventories These are equivalent (Within the context of a homogenous water mass) With GeoCAPE, we can probably track dPOC : dt

  4. Cruise data examples: Time-depth f-chlorophyll (mg/m3)

  5. Time-depth biological Oxygen Anomaly (μmol/m3)

  6. Data Processing • Raw oxygen and f-chl profiles were corrected with bottle data • bbp and c-660 derived particle inventories estimated using a regression with bottle POC • Integration to euphotic depth • Oxygen corrected for thermodynamic variability, air-sea flux and diffusion, then converted to carbon using the Redfield ratio

  7. Results: Doptically-derived particle inventories versus NCP r2 = 0.45 y = 0.1882x – 0.2243

  8. Results: Doptically-derived particle inventories versus NCP r2 = 0.85 y = 0.1432x + 0.0124

  9. Results: Doptically-derived particle inventories versus NCP r2 = 0.76 y = 0.1058x – 0.2125

  10. Conclusions for part 1 • The Good: Highly significant relationships between D optically derived particle inventories and NCP • The Bad: The relationships (in carbon units) should be 1:1, but are off by a factor 3 - 5 • The Ugly: We don’t know why (yet!)

  11. POCt1 POCt2 Part 2: Preliminary results of tracking particle inventories with a high resolution circulation model and MODIS 500m data Based on recent work: Estimating community productivity by tracking particle inventories in a Lagrangian context Jonsson, Salisbury, Mahadevan, Campbell (2009) Jonsson, Salisbury, Mahadevan (2011) (POCt2 - POCt1) NCP (t2 - t1) Premise:

  12. DPOC inventory gives an estimate of NCP

  13. Still to do on the GEO-CAPE Grant: • Use a 300m, hourly model and daily cloud free 250 and 500m MODIS data to: • Simulate differences in “net radiance production” between Eularian versus Lagrangian determinations over the course of a day. • Run the same simulation using increasingly large pixel resolutions.

  14. The high res domain, Casco Bay Maine (about 120x120 km) The first run using high res circulation and 500m MODIS (12:27 AM last night)

  15. 300m GOM-POM model (Salinity) Model output from Huijie Xue (UMO)

  16. How do our results help inform the GEO-CAPE SWG? • Results from part 1 suggest that sub daily changes in particle inventories can be use to to track daytime NCP rates • 3-5 determinations per day may be enough for daily NCP estimates provided the advective component is adequately resolved • For part 2: Preliminary work shows promise towards estimating rates from satellite tracking of particle inventories in a Lagrangian context. • In work still to be done, we anticipate considerable differences between the Lagrangian and Eularian approach (using high resolution data)

  17. Net community productivity (gC m2 d-1) mg C m-2 d-1

  18. Interpolation of a MODIS chl row over 5 days Linear Lagrangian Time (5days) Longitude

  19. Many assumptions but the biggest are: • Within the euphotic zone, along a Largrangian trajectory • POC = pCO2(bio) • 2. Phytoplankton POC : Chl = 53 • 3. Sinking, vertical mixing and DOC production by phytoplankton “excess production” are minimal, over short (2-7 day) time scales

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