A. Busalacchi, R. Murtugudde, W. Wang, C. Brown, J. Christian Ocean Ecosystem Modelling at ESSIC.

A. Busalacchi, R. Murtugudde, W. Wang, C. Brown, J. Christian

Ocean Ecosystem Modelling at ESSIC

Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere

Land surfaceLand surfaceLand surfaceLand surfaceLand surface

Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice

Sulphateaerosol

Sulphateaerosol

Sulphateaerosol

Non-sulphateaerosol

Non-sulphateaerosol

Carbon cycle Carbon cycle

Atmosphericchemistry

Ocean & sea-icemodel

Sulphurcycle model

Non-sulphateaerosols

Carboncycle model

Land carboncycle model

Ocean carboncycle model

Atmosphericchemistry

Atmosphericchemistry

Off-linemodeldevelopment

Strengthening coloursdenote improvementsin models

1975 1985 1992 1997

Th

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Offi

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Had

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Cen

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Towards Comprehensive Earth System Models

In a forcing sense, climate variability can influence:

• Circulation

• Temperature

• Stratification and Mixing

• Water mass characteristics

• Mesoscale activity

• Extreme events

• Basin-wide synchrony

In a forcing sense, climate variability can influence:

• Nutrient supply• New and export production pathways• Physiology (metabolic and reproductive

processes)• Age classes• Schooling patterns• Predator-prey interactions• Disease vectors

Flow chart for the ecosystem model and its coupling to the OGCM.

Global Biosphere from SeaWiFS – May, 2002

Attenuation depths from CZCS Pigments

Murtugudde et al., 2002

SST and surface current differences between CZCS and 17m att depth simulations.

Annual mean mixed layer depth differences between CZCS and 17m att depth simulations.

Temperature and current differences along the equator between CZCS and 17m attenuation depths.

Murtugudde et al., 2002

Flow chart for the ecosystem model and its coupling to the OGCM.The three-dimensional coupled physical-biogeochemical model (Christian et al. 2002), is based on the one-dimensional ecosystem model of Leonard et al. (1999) and the primitive-equation ocean model of Gent and Cane (1989).

The surface warming produced by the dynamical feedbacks are also reproduced in a forced ocean-biogeochemical model. The SST differences between the control run with Hp=17m and the simulation with biological feedback show a significant reduction in the cold-tongue bias. The trapping of radiation below the surface leads to dynamical feedbacks.

SST and current differences between runs with and without feedbacks between biology and physics

•The SST differences with andwithout bio-feedbacks havecorresponding decadal and ENSOrelated variability.

•Note that individual ENSO events can produce SST warming/coolingof over 0.5C which can be important in the coupled climatesystem.

• Ocean biology changes the depth of penetrating radiation

• Such changes influence ocean temperatures and circulation

• Do such changes feed back to the atmosphere in a coupled climate context?

• Next we couple our ocean model to a statistical atmosphere and the depth of penetrating radiation (HP) is determined from SeaWiFS observations:– Held fixed (control 17 m)– Annual mean (x,y)– Seasonally varying (x,y,t)

Ballabrera et al. (2006)

Control HP17m Annual mean HP(x,y) Seasonal HP(x,y,t)

Equatorial Sea Surface Temperature Anomalies

Time series

Power spectrum

Cycle length (years)

Time (years)

HP17 HPAM HPSC

NINO 3 (150oW-90oW, 5oN-5oS) Sea Surface Temperature Anomalies

Ballabrera et al., 2006

HP17

HPAM

HPSC

El Nino Frequency Distribution as Function of Time of Year

Control simulation with constant attenuation depth of 17m, and simulations with the annual mean and seasonally varying attenuation depths from satellite ocean color are carried out with an OGCM-Statistical atmosphere model. The annual phase-locking of the ENSO mature phase is accurately reproduced only when the seasonality is included.

COCO22 flux estimate, Takahashi et al., 2002 flux estimate, Takahashi et al., 2002

A physical-ecosystem-C model

OGCM:

Gent & Cane (1989)

Murtugudde et al.(1996)

Ecosystem model:

Christian et al.(2001)

Wang et al. (2004)

C chemistry model

C cycle in the ocean

Ecosystem/Carbon Model

• Variability

• Biogeochemistry & ecosystem

• Impact of physical forcing

• New (i.e. uptake of NO3)/Export production (sinking of organic material out of euphotic zone)

• Physical & biogeochemical controls

• ΔpCO2 and CO2 flux

• Model validation

• Underlying mechanisms

ΔpCO2 and CO2 flux (1ºN-1ºS)

•ΔpCO2 (40-180) •Highest in east•Strong seasonal & interannual variations

•Outgas 1-6 mol/m2/y •High in central area•Strong spatial & temporal variability

•Strong ENSO impact

Wang et al. 2006

ENSO has significant impact on ecosystem, biogeochemistry, and C cycle.El Nino: low Fe & low diss. CO2

low biomass,Z/P & low oceanic pCO2

low PP,NP,EP & low outgassing

La Nina: high Fe & high diss. CO2

high biomass,Z/P & high oceanic pCO2

high PP,NP/EP & high outgassing

NCEP

FSU

ECMWF

Zooplankton Distributions Preceding Bangladesh Cholera Outberaks: 1990-2006

De Magney et al., 2007

Ecological Forecasting

Salinity

SST Likelihood of Chrysaora

Habitat Model

1. Estimate current surface salinity and temperature fields

2. Georeference salinity and SST fields

3. Apply habitat model

4. Generate image illustrating the probable distribution of sea nettles

Predicted chance of encountering sea nettle, C. quinquecirrha, on August 17, 2007

Predicted relative abundance of Karlodinium veneficum on August 17, 2007

• Generate daily nowcasts of jellyfish, the harmful algal bloom Karlodinium veneficum, and Vibrio cholerae in Chesapeake Bay

• Nowcasts created by identifying the locations where ambient conditions coincide with the preferred environment (= habitat) of the organism

• Ambient environmental conditions required to drive habitat models

Ecological Prediction in Chesapeake Bay: Current Capabilities

Led by Chris Brown of NOAA

Prototype World Wide Web page of Pathogens in Chesapeake Bay

Ecological Prediction in Chesapeake Bay: Current Capabilities

Prediction are generated daily and are available on the World Wide Web

Transitioning to NOAA operations

Chesapeake Bay Forecast System

• Objective: Develop a fully integrated, coupled physical – biogeochemical model of the Chesapeake Bay and its watershed that assimilates in-situ and satellite-derived data by adapting and connecting existing models

• Purpose:– Near-Real Time Applications:

Nowcasting and forecasting of the Bay circulation, ecosystem, pathogens, harmful algal blooms, waves and inundation.

– Climate Projections: Estimating effect of climate change, between now and 2050, on the health of the Bay and its watershed.

SeaWiFS true-color image of Mid-Atlantic Regionfrom April 12, 1998.

Image provided by the SeaWiFS Project, NASA/Goddard Space Flight Center and ORBIMAGE

CBFS Design Concepts• Well-tested models combining physical climate and

biogeochemistry

• Open-source philosophy

• Modular structure

• Data Assimilation and optimization of the observing systems

• Easy portability to other regions (unification coastal prediction systems for the US)

• Incorporates future climate change projections from Intergovernmental Panel on Climate Change

CBFS StructureWeather Research & Forecasting (WRF)

Regional Ocean Modeling System (ROMS)

Soil and Water Assessment Tool (SWAT)

Using NCAR’s Weather Research & Forecasting (WRF) Modeling System

Land Surface Model

Choices Examined• NCEP - OSU - Air Force -

Hydrological Development Office of National Weather Service Land Surface Model (NOAH LSM)

• Hydrological Simulation Program--Fortran (HSPF)

• Soil and Water Assessment Tool (SWAT)

Chesapeake Bay Model

• Configured Regional Ocean Modeling System (ROMS) to Chesapeake Bay (ChesROMS)

Current Status of Model

ChesROMS• Implemented at CICS /

ESSIC

• 15-years worth of forcing data collected and formatted

• Validation and tuning of ChesROMS hydrodynamic variables complete

WRF• Ability to generate 48-hr

forecasts over domain at 20-km resolution

Land Surface Model• Initiate installation and

implementation of SWAT for CBay watershed

• Begin data search, compilation and formatting

NCEP Mission Statement

NCEP delivers analyses, guidance, forecasts and warnings for weather, ocean, climate, water, land surface and space weather to the nation and the world. NCEP provides science-based products and services through collaboration with partners and users to protect life and property, enhance the nation’s economy and support the nation’s growing need for environmental information.

Space Environment Center

Storm Prediction Center

Aviation Weather Center

NCEP Central Operations Climate Prediction Center Environmental Modeling Center Hydrometeorological Prediction Center Ocean Prediction Center

Tropical Prediction Center

NCEP Strategic Vision

Striving to be America’s first choice, first alert and preferred partner for climate, weather and ocean prediction services.

Model RequirementsModel Requirements

• CFS CFS WRF WRF

• DailyDaily

• ~ 10s kms~ 10s kms

• T, U, V, Precip, runoff, soil moisture, T, U, V, Precip, runoff, soil moisture, soil temp, nutrient loadingsoil temp, nutrient loading