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

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.

-73.8 -73.6 -73.4 -73.2 -73.0 -72.8 -72.6 -72.4 -72.2 -72.0 -71.8 -71.6

40.8

41.0

41.2

41.4

Connecticut

Long Island, NY

RI

Execution Rocks

Western Sound

Central SoundThames River

Ledge Light

LISICOS 05 Buoy ArrayLISICOS 05 Buoy Array

Eastern Sound

CODARCODARCOVERAGECOVERAGE

Norwalk Harbor

Flux buoys deployedJune - September, 2005

CODARCODARCOVERAGECOVERAGE

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

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 ???

• 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

Search and Rescue Challenges

Block Island Sound (BIS)CODAR Region

MACOORA Middle Atlantic Coastal Ocean Observing Regional Association

velocity

timet

position

tdt

d

p

pp

u

x

xux

),(

Drifter Trajectory Model:The Euler-Lagrange transformation

BIS DrifterTrajectory

24-Hour Trajectories

• Black: Actual SLDMB Trajectory

• Red: Trajectory Predicted From NOAA Currents

• Blue: Trajectory Predicted From CODAR Data

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

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

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

Eulerian Current Prediction Performance

GM 1,2 east

GM 1,2 north

BIS Final

Position Error

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 thefinal locations.

Red: drifter.Green: predictedassuming no CODARerrors. start

Conclusions1. STPS does better than no-motion and NOAA tides.2. Error budget is consistent and dominated by uncertainty in CODAR

– not the forecast algorithm3. Random-flight and STPS: slightly under-predict region of probable

Location5. Random-walk and STPS: severely under-predict region of

probable location

What is next?

•Improve CODAR uncertainty

•Integrate dynamics to the forecasts

DetailsO’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

A4

C2

Salinity in WLIS, 1995-2002A4 B3 C1 C2 D3 E1 F3

Distance from A4

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

SWEM 1989 Simulation

(Thanks to Grant McCardell)

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

15.54 9 K1RT TEMPERATURE COEFFICIENT 1.068 1.047 - 1.068 118 W *5.74 104 VSBAST TEMPERATURE CORRECTION 1.029 1.029 118 *5.55 1 K1CSATURATED PHYTOPLANKTON GROWTH RATE (AT TEMPERATURE = TOPT1)1.7 /DAY 1.7 - 2.5 118 W *4.79 110 VSSEDTTEMPERATURE CORRECTION FOR DEPOSITION TO SEDIMENT1.0294.72 72 K1113T TEMPERATURE COEFFICIENT 1.08 1.08 140 ***2.23 82 K1516T TEMPERATURE COEFFICIENT 1.08 1.08 142 ***

1.55

95 FLOCEXFRACTION OF PRIMARY

PRODUCTIVITY GOING TO LABILE ORGANIC CARBON VIA EXUDATION

0.2

1.54 70 K1012T TEMPERATURE COEFFICIENT 1.08 1.08 140 ***1.51 74 K1213T TEMPERATURE COEFFICIENT 1.08 1.08 140 ***1.47 107 VSPMT TEMPERATURE CORRECTION 1.0291.30 8 K1RCENDOGENOUS RESPIRATION RATE AT 20 DEG C0.085 /DAY 0.1 - 0.3 118 W *1.26 44 KMPHYTHALF SATURATION CONSTANT FOR PHYTOPLANKTON0.0 MG C/L 0.05 133 ***1.13 4 IS1 SATURATING ALGAL LIGHT INTENSITY 150.0 LY/DAY1.02 24 TOPT2OPTIMAL GROWTH TEMPERATURE FOR SUMMER ASSEMBLAGE26.0 DEG C 20.0 - 25.0 118 S *

0.95

101 KAT TEMPERATURE CORRECTION

COEFFICIENT FOR ATMOSPHERIC REAERATION

1.024

0.89 76 K1314T TEMPERATURE COEFFICIENT 1.08 1.08 140 ***0.89 16 CRBN11CARBON TO NITROGEN RATIO - NON-N LIMITED5.67 MG C/MG N5.2 -5.67 126 W *0.80 54 FNH3 AMMONIA 0.35 0.15 - 0.35 140 ***

REFERENCE

SOURCE

CONSTANTS : NAMES / DESCRIPTIONS / VALUES AND REFERENCES

Sensitivity d/dC(Skill)

NO NAME DESCRIPTION VALUES UNITSREFEREN

CE VALUE

REFERENCE PAGE

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

hPhRBz

cKcdzu

t

Ch

h

.

Tendency of tidally averagedlayer average

‘DispersiveHorizontal Flux”

Turbulent Flux across pycnocline

Benthicdemand

Vertical integralof respiration

X(its dark)

Parameter EstimatesRespiration:

R= 8.6 mMoles/m3/day in July

R=19.5 mMoles/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= 40 mMoles m-2 day-1

1-2-3

daym mMoles20/

100

)200400(10

days

mmMm

dt

dCh

Rate of Change

PAR (WLIS)

MET (Ex Rocks, WLIS, CLIS, LedgeLight)

Datalogger, batteries,

Near-surface sensorT,S,DO,PAR,ChlA

Mid-waterT,S,DO,PAR,ChlA

Near-bottom sensorT,S,DO

NOT TO SCALE

EX Rock mooringJul Aug Sep Oct

0

100

200

300O

2 Sat

urat

ion

Jul Aug Sep Oct0

5

10

15

20

O2 C

once

ntra

tion

Rapid decreases

• 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

Figure 9

(a)

(b)

(c)

t

T

t

DO

t

S

??

Mixing eventsAdvective events

Rh

B

z

cKh

dzuht

C

h

1

.1

-2 = Dispersion + mixing - 4 - 15 (mMoles/m3/day)

Season Scale Balance

21 = 40 - 4 - 15 (mMoles/m3/day)

-19 = 0 - 4 - 15 (mMoles/m3/day)

Respiration/Minimum transport mode

Ventilation/Maximum transport mode

Subtidal Balance

(assuming respiration is constant)

Figure 11

Wind Stress

Wind Stress Component

Negative Along Sound Stress Coincides with Ventilation intervals.

What Causes Ventilation Events?

The effect of along shelf wind

zK

zxz

u

zt

2

2

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.

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

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