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
e M
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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