290 likes | 392 Views
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
E N D
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.
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)
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
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
Results: Doptically-derived particle inventories versus NCP r2 = 0.45 y = 0.1882x – 0.2243
Results: Doptically-derived particle inventories versus NCP r2 = 0.85 y = 0.1432x + 0.0124
Results: Doptically-derived particle inventories versus NCP r2 = 0.76 y = 0.1058x – 0.2125
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!)
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:
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.
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)
300m GOM-POM model (Salinity) Model output from Huijie Xue (UMO)
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)
Net community productivity (gC m2 d-1) mg C m-2 d-1
Interpolation of a MODIS chl row over 5 days Linear Lagrangian Time (5days) Longitude
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