Long Island Sound Integrated Coastal Observing System

1. The Big I in LISICOS:A Brief History of the Development of a Coastal Observing System and Some Interesting Products James O’DonnellUniversity of Connecticut

2. Long Island Sound Integrated Coastal Observing System Goal: The development of products for the safe, wise and sustainable use of the ocean using • a coherent sustained time-series observation program • short periods of more intensive process studies, • the development of a data center, • development and assessment of models of oxygen and nutrient cycles, circulation, and water properties, • instrument an method development • and outreach programs to enhance of partnerships with State, Federal and local governments and citizens.

3. 41.4 41.2 41.0 40.8 -73.8 -73.6 -73.4 -73.2 -73.0 -72.8 -72.6 -72.4 -72.2 -72.0 -71.8 -71.6 LISICOS 05 Buoy Array Thames River Central Sound Ledge Light Norwalk Harbor Western Sound RI Connecticut CODAR COVERAGE CODAR COVERAGE Long Island, NY Flux buoys deployed June - September, 2005 Eastern Sound Execution Rocks

4. http://lisweb.dms.uconn.edu/website/lisicos

5. Observatory Activities • Surface Current Prediction - USCG • Understanding Hypoxia – EPA and CTDEP • Real time data dissemination • Comprehensive data center And in the near future • Flooding – Climate change – Invasions ???

6. Search and Rescue Challenges • Coastal Currents Are Complex • Highly Variable in Time - Tides • Highly Variable in Space - Topography • Limited Sources of Coastal Currents • NOAA Tidal Currents at Inlets • Variety of Coastal Model Products • Site-specific, costly, and not ready for operational use • CODAR Can Provide Surface Current Maps (Hourly – 1-3 hour latency) • CODAR Sites Are Available, Expanding PART 3

7. Block Island Sound (BIS)CODAR Region

8. MACOORA Middle Atlantic Coastal Ocean Observing Regional Association

9. Drifter Trajectory Model:The Euler-Lagrange transformation

10. BIS DrifterTrajectory

11. 24-Hour Trajectories • Black: Actual SLDMB Trajectory • Red: Trajectory Predicted From NOAA Currents • Blue: Trajectory Predicted From CODAR Data

12. Practical Application Requires a FORECAST • Develop a transportable, data based, current and trajectory forecast system • Make it operational • Evaluate it relative to current practice • Make it available to operators • Figure out how to sustain the system

13. Short Term Prediction Algorithm • Recognize Currents have Tides and ‘Sub-tidal’ components • Harmonic Analysis for Tides • Hedging, or Gauss-Markov Estimation for Subtidal Currents • Lots of details about Autocorrelation Estimation • Forecast 24 hours every hour • Euler-Lagrange transformation to get trajectories

14. Summary of (some) Algorithms • Hedging (24 hr running mean) • GM 1 - No covariance between u & v and spatial average of coefficients • GM 2 – Covariance of u & v included and no spatial averaging of coefficients • Wind was added but no substantial improvement in skill was detected

15. Eulerian Current Prediction Performance GM 1,2 east GM 1,2 north

16. BIS Final Position Error

17. Comparison of RW & RF Simulations • For each trajectory segment, simulate 1000 trajectories: • Blue dots represent endpoints of simulated trajectories. • Region comprising gray rectangles enclose 95% of the • final locations. Red: drifter. Green: predicted assuming no CODAR errors. start

18. Conclusions • STPS does better than no-motion and NOAA tides. • Error budget is consistent and dominated by uncertainty in CODAR • – not the forecast algorithm • Random-flight and STPS: slightly under-predict region of probable • Location • 5. Random-walk and STPS: severely under-predict region of • probable location • What is next? • Improve CODAR uncertainty • Integrate dynamics to the forecasts

19. Details O’Donnell, J, D. Ullman, C. Edwards, T. Fake and A. Allen (2005), Operational Prediction of Lagrangian Trajectories in the Coastal Ocean Using HF Radio Derived Surface Currents. J. Atmos. and Oceanic Tech. (Accepted with revisions) Ullman, D.S., J. O’Donnell, J. Kohut, T. Fake, and A. Allen (2005). Trajectory Prediction using HF Radar Surface Currents: Monte Carlo Simulations of Prediction Uncertainties. Geophys. Res. (In Press) Ullman, David, James O’Donnell, Christopher Edwards, Todd Fake, David Morschauser, Michael Sprague, Arthur Allen, LCDR Brian Krenzien, (2003). Use of Coastal Ocean Dynamics Application Radar (CODAR) Technology in U. S. Coast Guard Search and Rescue Planning, US Coast Guard report CG-D-09-03. http://www.rdc.uscg.gov/reports/2003/2003-0559report.pdf O’Donnell, J., D. Ullman, M. Spaulding, E. Howlett, T. Fake, P. Hall, I. Tatsu, C. Edwards, E. Anderson, T. McClay, J. Kohut, A. Allen, S. Lester, and M. Lewandowski (2005). Integration of Coastal Ocean Dynamics Application Radar (CODAR) and Short-Term Predictive System (STPS) Surface Current Estimates into the Search and Rescue Optimal Planning System (SAROPS). U.S. Coast Guard Technical Report DTCG39-00-D-R00008/HSCG32-04-J-100052 http://www.rdc.uscg.gov/reports/2005/2005-1005-public-rdc671.pdf

21. C2 A4

23. Salinity in WLIS, 1995-2002 A4 B3 C1 C2 D3 E1 F3 Distance from A4

24. DO climatology in WLIS, 1995-2002 A4 B3 C1 C2 D3 E1 F3

25. SWEM 1989 Simulation (Thanks to Grant McCardell)

26. From Hydroqual, 2004 – User’s guide to RCA (release 3.0)

28. X(its dark) ‘Dispersive Horizontal Flux” Benthic demand Tendency of tidally averaged layer average Turbulent Flux across pycnocline Vertical integral of respiration • The Simplest Model: • Torgersen, De Angelo and O’Donnell (1997), Estuaries Vol. 20, No. 2, p. 328-345 June 1997 • Integrate oxygen transport vertically from the bed to the pycnocline • Average in time over a tidal period, • Neglect transport by mean flow and production in the layer

29. Rate of Change Parameter Estimates Respiration: R= 8.6mMoles/m3/day in July R=19.5mMoles/m3/day in Aug WELSH, B. L. AND F. C. ELLER. 1991. Mechanisms controlling summertime oxygen depletion in western Long Island Sound. Estuaries 14:265-278. Benthic Demand: B= 40mMoles m-2 day-1

30. MET (Ex Rocks, WLIS, CLIS, LedgeLight) PAR (WLIS) Datalogger, batteries, Near-surface sensor T,S,DO,PAR,ChlA Mid-water T,S,DO,PAR,ChlA Near-bottom sensor T,S,DO NOT TO SCALE

31. EX Rock mooring Rapid decreases

32. Observations show intervals of ventilation and then respiration. • There is little evidence of significant variation in community respiration. • Oscillations are likely a result of variations in mixing and advection

33. Mixing events Advective events (a) (b) (c) ?? Figure 9

34. Subtidal Balance (assuming respiration is constant) Respiration/Minimum transport mode -19 = 0 - 4 - 15 (mMoles/m3/day) Ventilation/Maximum transport mode 21 = 40 - 4 - 15 (mMoles/m3/day) Season Scale Balance -2 = Dispersion + mixing - 4 - 15 (mMoles/m3/day)

35. Negative Along Sound Stress Coincides with Ventilation intervals. What Causes Ventilation Events? Wind Stress Wind Stress Component Figure 11

36. The effect of along shelf wind

37. Summary and Conclusion • The seasonal-scale DO trend is a consequence of repeated 2-5 day cycles of respiration and ventilation • Ventilation intervals are associated with along Sound winds towards the East River • Wind effect consistent with modulation of the rate of re-stratification by the estuarine circulation.

38. Implications • Vertical mixing during ventilation also transports nutrients up and plankton down. • Perturbs/Modulates the population dynamics and biogeochemistry. • Understanding the seasonal scale requires understanding the 2-5 day scale population and biogeochemistry

39. OBSERVATORY LESSONS • Money for observations is motivated by problems and products • Science research and understanding is a product – perhaps the biggest driver of infrastructure investment • Generic monitoring/data acquisition is never going to be high priority for substantial investment • Generic monitoring will never satisfy anyone’s needs