# Presentation: 526699

## Circulation Models &         Their Importance for       Ocean Observations

- Cisco Werner
- University of North Carolina Chapel Hill

**Integrated Ecosystem Observations: the Future in IOOS (Aug 23, 2005)**

## Outline

- Introduction
- Status of models (examples)
- Challenges
- Regional observing systems
- Concluding remarks

## Properties of circ’n models

- Capture “ecologically relevant” scales
- 100’s of meters to 100’s of kms in the horizontal
- O(1-10 meters) in the vertical
- Time-scales of hours to months
- Horizontal and vertical dimensions should be explicit, i.e., must include 3-D
- Realistic representation of the domain
- Hydrographic structure (Temperature and Salinity effects)
- Ability to force or link to ecosystem models

## Focus (herein) on coastal domains

- Many marine ecosystems of interest are mainly in coastal regions
- Commercial fisheries and human impacts are perhaps greatest
- However, coupling with open ocean and basin scales is essential to capture coastal dynamics

## Models: fill in gaps in space and time (and provide estimates) where observations are not available

- (http://www.seacoos.org)

## Data gaps:

- 	The depth-dependent (vertical) component is undersampled

- (http://www.seacoos.org)

## Vertical structure 

- Vertical structure

- (Aretxabaleta et al. 2004)

- (Lough et al. 2004)

## Vertical structure of the circulation 

- Wind effects

- 1 meter

- 10 meters

- 30 meters

- 50 meters

- Decreased wind effects

- Wind

- Vertical structure of the circulation

- (Werner et al. 1993)

## Behavior of organisms

- Near surface “loss”

- Sub-surface “retention”

- (Werner et al. 1993)

## Models hindcast past states of circulation and ecosystem to understand previous and present scenarios

- Difference of mean winter (Jan-Feb-Mar) MLD(1977-2000 minus 1952-1975)

- (Aita-Noguchi et al. 2004)

## Interdecadal variation of the lower trophic ecosystem in the northern Pacific between 1948 and 2002, in a 3-D implementation of the NEMURO model.

- (Aita-Noguchi et al. 2004)

## Pacific Herring weight (g-ww/ind)

- (Rose, Megrey, Werner et al. 2005)

## Models:  long-term forecast of future states 

- Models:  long-term forecast of future states

- A1B

- B1

- Commitment

- (Yamanaka et al. 2004)

- (Hurrell. 2005)

## Models provide nowcasts & short-term forecasts of the state of the system 

- (http://efsis.rsmas.miami.edu/forecast.html)

## Present state of circ’n models

- 	Forced by atmospheric fields, tides, river discharge and (climatological) hydrography, models can “robustly” capture key circulation components

## NO3

- NO3

- Chlorophyll

- Large
- detritus

- Organic matter

- N2

- NH4

- NO3

- Water column

- Sediment

- Phytoplankton

- NH4

**Mineralization**

**Uptake**

**Nitrification**

**Nitrification**

**Grazing**

**Mortality**

- Zooplankton

- Small
- detritus

**Aerobic mineralization**

**Denitrification**

- Biogeochemical sub-model (K. Fennel)

## Northeast North Atlantic (NENA)             embedded within NATL basin model

- 3-day average open boundary values from NATL7-component NPZD ecosystem

**Temperature**

**Chlorophyll**

## Observational estimates of mean annual shelf primary production:

- _Observational estimates of mean annual shelf primary production_:
- LEO15: 715 gC m-2 y-1                 	(MacDonald 1983)
- NY Bight: 290 gC m-2 y-1
- 	(Malone et al. 1983)
- G.o.Maine to Cape Hatteras:
- 310 gC m-2 y-1
- 	(O’Reilly and Busch 1984)
- Southern MAB: 620 gC m-2 y-1 	(Lohrenz et al. 2002)

## Challenges: Data needs to initialize mass fields, i.e., how does one specify initial T & S? (as well as biological & biogeochemical variables)

- http://hycom.rsmas.miami.edu/

- (Blanton et al. 2004)

## Challenges: Linking open ocean and shelf regions – observations and models

- (http://www.skio.peachnet.edu)

- (provided by J. Nelson)

## OPEN

**OPEN**
**OCEAN**

**SHELF SEAS**

- Climate forcing of
-  ocean circulation

- (Heath et al.)

## Open ocean models as forcing for shelf domains (and source/sink of “biology”) – need for nesting

- http://hycom.rsmas.miami.edu/

## Nesting across model domains using different models

- Nesting across model domains using different models

- (provided by B. Blanton)

## Challenges - forecasting

- Forecasting for periods longer than one week
- Models capture correct dynamics and produce realistic features – but they occur “at the wrong time and at the wrong place”
- Limitations set by
- the skill of atmospheric forecasts
- initialization of oceanographic model
- oceanic forcing data (open boundaries)
- missing internal dynamics
- Consider data assimilation for additional information (not as a fix for missing dynamics)

## Data assimilation

**Data assimilation**

- (Lynch et al. 2000)

## Prior

**Prior**
**Separation: 1.78 Km/day**

**Posterior**
**Separation: 0.80 Km/day**

- (Lynch et al. 2000; Aretxabaleta et al. 2004)

- Assimilate drifter trajectories

## 	How far are we along toward achieving an integrated, operational, observational and modeling system of oceanic ecosystems?

## Integrated observations and models

- (Based on Heath & Gurney)

## SEACOOS integration of modeling and observations

- USF

- UM

- UNC

## Sea Level & Current meter comparisons

**POM, ROMS, & FVCOM ****hindcast comparisons with data.**

**Sea Level & Current meter comparisons**

**BAROTROPIC                BAROCLINIC**

## High-resolution models for estuary-shelf interactions

**High-resolution models**** for estuary-shelf interactions**
**and hurricane storm surge simulations.**

**2.6Km/day**
**2.0Km/day**
**1.8Km/day**
**0.8Km/day**

**LARVAL DISPERSAL STUDIES (CORAL REEFS, MPAs) **

## Focus on the Summer 2003 cool event for hindcast case

**Nesting**** with basin-scale HYCOM & NCOM models **
**Data assimilation**
**    ****statistical**
**    ****adjoint**
**CODAE planning**
**HF radar-derived   **
**    ****surface currents**
**Surface drifters**
**Velocity profilers from moorings**
**T/S fields from profilers and gliders**

**Focus on the Summer 2003 cool event for hindcast case**

## SoFLA-Regional HYCOM,Florida Bay EFDC and TIME models

- SoFLA-Regional HYCOM,Florida Bay EFDC and TIME models

- (Ortner, Johns, Lee and Kourafalou)

**Notes:**
Surveys obtain optical and water quality (nutrient) data along track and the Optics on the moorings here relate to transmittance and chlorophyll measurement. Some moorings along the SW Shelf will also be fitted with in situ genetic probes for harmful algal blooms being developed at AOML with Oceans and Human Health funding. 

These models are currently hydrodynamic (HYCOM and EFDC) or hydrological (TIME) but all are being adapted to add nutrients  (N and P) and in the case of EFDC (PAR, phytoplankton blooms and submerged aquatic vegetation. Moreover a specified set of ecological models for upper trophic levels driven by “water quality” fields (N,P, chlorophyll, PAR, SAV fields is already available for use within this domain.  These are used to generate forecasts for key indicator species.

## Concluding remarks (1/2)

- Models are an essential component of observing systems.  They:
- integrate across space and time scales and provide information that present observing systems cannot
- provide forecasts
- Certain aspects of modeling systems are robust and provide useful information, such as:
- Lagrangian characterization of flow (e.g., to provide input for the design of MPAs)
- Hindcasts and short-term nowcasts and forecasts
- Challenges include
- Initialization of hydrographic and biogeochemical fields (sustained, increased measurements)
- Coupling with open ocean (e.g., GODAE & CODAE)

## Concluding remarks (2/2)

- Design of observing and modeling systems should not proceed separately (e.g., CODAE)
- Models can guide the design observing system
- Data provide models with needed information for assimilation, etc.
- Achieving operational and sustained status remains a significant challenge requiring a great deal of additional research.  Needs include:
- instrumentation (always need more data! – but need to be efficient about the instruments deployment)
- model integration across models (e.g., atmospheric, oceanic, etc.) and with available data
- use of new (e.g., Grid) technologies
- personnel to run the systems and to _*interpret the results*_