ó á ó á Aproximación a la dinámica de Ecosistemas Marinos

Aproximación
ó a la dinámica
á
de
Ecosistemas Marinos
Coastal Zone Ecosystem Management
Coastal zone management requires a good understanding of marine
ecosystem dynamics
y
are constituted by
y a large
g number of components
p
Marine ecosystems
showing complex interactions.
Simplifying the marine ecosystem structure and its dynamics is helpful.
Máster Internacional en Gestión de Zonas Costeras y Estuáricas
A way for such simplification is through conceptual models.
LIM-UPC
Modelling interactions of ecosystem components allows getting an
approach
h to
t ecosystem
t
d
dynamics.
i
Nixon Bahamón
An approach, but not a good understanding, on the ecosystem dynamics
can be reached in a couple of teaching hours.
bahamon@ceab.csic.es
www.marinecometrics.com
www
marinecometrics com
www.ceab.csic.es/~oceans
Here we go!
Centre d’Estudis Avançats de Blanes (CEAB(CEAB-CSIC)
Barcelona, 15 de febrero de 2010
Ecosystem--based management
Ecosystem
1-6: Dominio pelágico
1. Región nerítica; 2. Región oceánica; 3. Zona Epipelágica.
Epipelágica.
4. Zona Batial (4a
(4a.. Zona Mesopelágica;
Mesopelágica; 4b
4b.. Zona Batipelágica);
5. Zona Abisopelágica o Abisal; 6. Zona Hadalopelágica o Hadal
Hadal;;
(t: termoclina permanente)
Source: http://www.ebmtools.org
3
A-D: Dominio bentónico
A. Plataforma continental; B. Talud continental (B1
(B1.. Talud continental superior;
B2
B2.. Talud continental inferior); C. Llanura abisal; D. Fosa hadal.
hadal.
4
Fuente: Wikipedia
Clasificación de los ecosistemas marinos
Según la distancia a la costa
• Zona nerítica: desde la línea de la costa hasta el borde de la plataforma
p
continental.
• Zona oceánica: fuera del límite de la plataforma continental.
Según la profundidad
• Zona fótica: zona iluminada.
– Zona epipelágica:
pp g
hasta el límite de la p
plataforma continental ((200 m de
profundidad). Tiene lugar la producción primaria (fotosíntesis).
• Zona afótica: zona oscura.
– Zona mesopelágica: 200 - 1.000 m. Abundante zooplancton. Se
encuentra la termoclina permanente.
– Zona batipelágica: 1.000 - 3.000 m.
– Zona abisopelágica o abisal: 3.000 - 6.000 m.
– Zona hadopelágica o hadal: más de 6.000 m; fosas oceánicas
Clasificación de los ecosistemas marinos
Sistema bentónico (fondo marino)
El fondo marino ((rocoso,, p
pedregoso,
g
, arenoso,, fangoso)
g
) está p
poblado p
por
organismos bentónicos.
.
• La región fótica:
– Zona supralitoral (no sumergida)
– Zona mesolitoral (intermareal)
– Zona sublitoral: (p
(permanentemente sumergida
g
en la p
plataforma))
•
La
–
–
–
–
región afótica:
Zona circalitoral: ((externa de la p
plataforma sin vegetación)
g
)
Zona batial: (talud continental entre 200-3.000 m.)
Zona abisal: (fondo oceánico, llanuras oceánicas, entre 3.000-6.000 m.)
Zona hadal: (Zonas de subducción o de fosasa oceánicas 6.000 - 10,000
m)
5
Gulf of Lions
Catalan Sea
MFSPP-VOS
MFSPPCruises
Black Sea
Adriatic Sea
Tyrrhenian Sea
6
Aegean Sea
Alboran Sea
Ionian Sea
Levantine basin
AQUA--MODIS Sea
AQUA
Sea--surface chlorophyll,
chlorophyll,
March 2009
(Source
Source:: http://oceancolor.gsfc.nasa.gov)
Gulf of Lions
Catalan Sea
Alboran Sea
Adriatic Sea
Black Sea
Tyrrhenian Sea
Aegean Sea
Ionian Sea
Levantine basin
AQUA--MODIS Sea
AQUA
Sea--surface chlorophyll,
chlorophyll,
September 2009
(Source
Source:: http://oceancolor.gsfc.nasa.gov)
Source::
Source
A. Cruzado, Chief Sci
Sci..
Ocean.
Ocean. Lab.,
Lab., CEABCEAB-CSIC.
7
Prep.. by L. Simic
Prep
25/01/01, Blanes, Spain
8
What drives ocean circulation?
Global surface current system
10
Open University, Ocean Circulation, 2007
The Ocean Conveyor Belt
What drives ocean circulation?
Seawater flows along the horizontal plane and in the vertical:
Typical speeds of the horizontal flow or currents: ~ 0.01-1.0 m/s
yp
vertical speeds
p
within the stratified ocean: ~ 0.001 m/s
Typical
1. Wind driven circulation: The wind exerting a stress on the sea surface
induces movement of that water. This is called Ekman Layer transport,
which extends to the surface 50 to 200 meters.
The wind driven circulation is characterized by large clock-wise and counter
clock-wise flowing gyres, such as the subtropical and sub polar gyres.
2 Thermohaline circulation: Buoyancy (heat and freshwater) fluxes between
2.
the ocean and atmosphere that alter the density of the surface water.
The thermohaline circulation engages the full volume of the ocean into the
climate system, by allowing all of the ocean water to 'meet' and interact
directly the atmosphere (on a time scale of 100
100-1000
1000 years).
Ocean circulation driven by density differences. (Density is controlled by ocean temperature
and saltiness.) Cold, dense water in the Arctic merges with salty water from the Gulf
Stream to create the sinking North Atlantic Deep Water (NADW) in the NorwegianGreenland Sea
Sea. The NADW helps to drive global ocean circulation
circulation.
Illustration The M Factory © Smithsonian Institution.
From: http://forces.si.edu/arctic/02_02_04.html
3. Geostrophic Currents: The ocean currents are for the most part
geostrophic, meaning that the Coriolis Force balances the horizontal
pressure gradients.
gradients
4. Inertial Currents: Curve motion produced by the Coriolis force when wind
ceases to blow.
http://eesc.columbia.edu/courses/ees/climate/lectures/o_circ.html
Sverdrup's
p Theory
y of the Oceanic
Circulation
• Answers to the questions can be found in a series of three
remarkable papers published from 1947 to 1951.
• In the first,
first Harald Sverdrup (1947) showed that the
circulation in the upper kilometer or so of the ocean is
directly related to the curl of the wind stress.
Ecuaciones de Momento
En dinámica de fluidos, las ecuaciones de momento describen el
movimiento de un fluido compresible no viscoso.
viscoso
q ≈ Euler eq.q sin Fr))
Ecuaciones de momento = Navier-Stokes eq.
Gradiente de presión
Fuerza de Coriolis
(~7.3 x105 radianes s-1
• Henry Stommel (1948) showed that the circulation in
oceanic gyres is asymmetric because the Coriolis force
varies with latitude.
• Finally, Walter Munk (1950) added eddy viscosity and
pp layers
y
of the Pacific.
calculated the circulation of the upper
Together the three oceanographers laid the foundations for
a modern theory of ocean circulation.
latitud
fricción
http //ocean o ld tam ed / eso ces/ocng te tbook/chapte 11/chapte 11 01 htm
http://oceanworld.tamu.edu/resources/ocng_textbook/chapter11/chapter11_01.htm
Harald Sverdrup (1947). The Oceans: Their Physics, Chemistry and General Biology
Circulación termohalina y transporte de
partículas
Molar redfield ratios
Δ P: Δ N: Δ Si: Δ C = 1:16:15:106 (Brzezinski,
Brzezinski, 1985).
Open University, Mar.Biog.Cycles, 2005
16
Parámetro conservativo PO
(Fosfato preformado)
Utilización aparente de oxígeno (AOU)
17
Niveles troficos del ecosistema marino
18
Fishing grounds in a benthic environment
Barcelona
Th
hrophic
c level
Iberian
peninsula
The Blanes Canyon
Western Mediterranean
Sea
In the Blanes canyon A. antennatus
dwells from 600 to 900 m depth,
coinciding with the lower boundary
of Levantine Intermediate Water
(LIW) and the upper boundary of
Western Mediterranean Deep Water
(WMDW).
The Blanes canyon
y
Fuente: Sarda et al. 2009.
Prog. Oceanog. 82: 227-238
Coll et al., 2008
Ecosistema pelágico
Seasonal changes
g in upper
pp water layers
y
EUPHOTIC ZONE
EUPHOTIC ZONE
MIXED-LAYER
Phy -N
NO -N
N
Zoo -N
NH4 -N
NO -N
200 m
NO3-N
winter
Summer phytoplankton chlorophyll in the 24.5°
North Atlantic WOCE section
01
.02
02 .03
03 .05
05
.11
.22 .33
.55
1
MIXED-LAYER
Phy -N
N
NO -N
N
Zoo -N
NH4 -N
NO -N
Ph -N
Phy
N
NO -N
N
Zoo -N
NH4 -N
NO -N
Phy -N
NO -N
N
Zoo -N
NH4 -N
NO -N
NO3-N
NO3 -N
NO3 -N
spring
summer
autumn
Surface chlorophyll a and trophic levels of the oceans
2
Eutrophic
Mesotrophic
SeaWiFS
Station 101
90
83
75
67
50
59
42
35
27
20
12
-50
Chl a
(mg m-3)
1
Oligotrophic
-100
Depth (m)
D
-150
-200
-250
-300
-350
-400
-450
-75°
Chlorophyll a (mg / m3)
-70°
-65°
-60°
-55°
-50°
-45°
Longitude
Bahamon et al., 2003
-40°
-35°
-30°
-25°
-20°
A.Morel
(1996)
S Nixon
S.
(1995)
Surface Clorophyll a
Oligotrophic 0.05 mg Chl a m-3
M t hi 0.5
Mesotrophic
0 5 mg Chl a m-33
Eutrophic
>1 mg Chl a m-3
Depth-integrated PP
<0.5 g C m-2 d-1
0 5 -1.5
0.5
1 5 g C m-22 d-11
1.5 - 2.5 g C m-2 d-1
Observation, Analysis and Modeling of Marine Systems
Daily data reception &
Publication on the web
Temperature
Humidity
Irradiance
Wind speed
& direction
Pressure
Temperature
GPS
Data
processing
Chlorophyll
•Mixed layer models (Evans - Parslow, 1985; Fasham et al., 1990)
Solar panels
Data
processing &
assimilation in
numerical
i l
models
Lab
analysis
Phone card
Data logger
Batteries
Field sampling
on board R/V &
VOS
T, S
IM1
25 m
Mooring Site
Blanes Station
3D
Coupled
Physical
Biogeochemical
M d l
Model
Current-meter
IM2
50 m
•Vertically
V
i ll resolved
l d models:
d l
z-dependent: z-level systems (~1 to 5 m layer thickness)
with
ith turbulent
t b l t diffusion
diff i
parameterisations
t i ti
(Varela
(V
l ett al.,
l
1994; Oguz et al., 1996; Levy et al., 1998; Bahamon and
Cruzado, 2003, etc…)
CEAB-CSIC
1 DV
Coupled
Physical
Biogeochemical
Model
Vertical resolution of models
Antenna
Bi--directional communication
Bi
Temperature
Salinity
PAR
Dissolved
Oxygen
Turbidity
Chlorophyll
sigma-dependent: vertical coordinates are layers following
terrain; of common use in ocean circulation models (e. g.
Mellor and Yamada
Yamada, 1974; Zavatarelli et al
al., 2000,
2000 Ahumada
& Cruzado, 2007, etc...)
Isopycnal-dependent:
Isopycnal
dependent: vertical coordinates are isopycnals
Operational Oceanography, CEAB-CSIC
Grids used in 3D models
Some model references: Y. Tony Song & Yi Chao. 1999; Blumberg & Mellor. 1987;
27
Schopf, PS. 1995, etc.
Figures from Open University, Ocean Circulation, 2007
z vs. sigma coordinate models
z vs. sigma coordinate models
Simulation of the vertical temperature
in an area of the Algerian Sea
Depth (m)
D
-50
-100
-150
-200
-250
Model results
300
30
360
330
120
90
60
Time ((days)
y )
Bahamon, 2002
The nitrogen cycling in a pelagic ecosystem
Heat,wind
wind stress,
H2O,H
N22,0,
O2 N
, CO
Heat,
stress,
2 ,2 O2 , CO2
Atmosphere
ATMOSPHERE
Los modelos biogeoquímicos representan un conjunto de
interacciones entre procesos biológicos, geológicos y químicos
Ocean
OCEAN
Small p
phytoplankton
y p
Un modelo acoplado representa la interacción de elementos bióticos
y abióticos con diferentes aproximaciones
(relaciones funcionales)
Modelo físico +
bio-geo-químico
bio
geo químico o biológico o ecológico o =
Modelo acoplado
Small zooplankton
p
Grazing
Grazing
Later
ral
bounddary
LATER
RAL BOUND
DARIES
El acoplamiento de los procesos biogeoquímicos y ecológicos a los
procesos hidrodinámicos (medioambientales) dan como resultado
un modelo acoplado
Large phytoplankton
Uptake
Mortality
Exudation
DIN
Mortality
+ Fecal
pellets
Excretion
NO2
Nitrification
NO
3
DON
NH
4
PO N
Sinking
Excretion
Predation
Large zooplankton
Excretion
Uptake
Laterall boundary
ry
Modelos biogeoquímicos
g q
Mortality
Bacteria
Mineralisation
DEEPER WATERS
Fasham et al., 1990
Comparison between surface Chl
Chl--a from a 3D model and
satellite observations in NW Mediterranean Sea
Cambio instantáneo de una población:
Modelo biológico
Nt+1 = Nt - (d+e) + (b+i)
La población de una especie en un momento determinado (Nt+1)
(i.e. una microalga seleccionada como posible indicadora
ambiental) está determinada por:
el número actual de individuos (Nt)
menos el número de individuos que mueren (d) o emigran (e),
más los individuos que nacen (b) e inmigran (i)
Bernardello et al. 2007
Cambio instantáneo de una población:
Modelo biológico y físico (acoplado)
Fases para la implementación de un modelo
ecológico
• Calibración
(
)
∂PHY
∂ ⎡
∂PHY ⎤
∂PHY
K
+
=
− w+w
⎢
⎥
s ∂z
∂t
∂z ⎣ z ∂z ⎦
(
+ PHY U
NO3
+U
NO2
+U
NH4
)− PHY EXU − G
La variación temporal de un grupo funcional (PHY, fitoplancton) dependerá de
componente difusivo (…K
( Kz…)
menos las pérdidas por advección vertical y hundimiento de las células (…w+ws…)
más factores biológicos:
consumo de nitrógeno
g
menos pérdidas de nitrógeno y consumo por parte del zooplancton
– ¿E
¿Es adecuada
d
d lla parametrización
t i
ió d
dell modelo?
d l ?
– ¿El modelo reacciona como se espera?
• Verificación
V ifi
ió
– ¿El modelo es estable a largo plazo?
– ¿ Conserva la masa?
• Validación
– ¿Los datos observados se corresponden con los estimados?
– Análisis
A áli i cualitativo
lit ti
y cuantitativo
tit ti
d
de lla simulación
i
l ió en relación
l ió
con las observaciones.
• Sensibilidad
– Sensibilidad a las formulaciones, parámetros, constantes,
submodelos, variables de estado.
– Análisis estadístico de las simulaciones en relación a la
sensibilidad de parámetros, etc.
Example: 1DV model of the
oligotrophic pelagic environment
Model description
Irradiance,
lenght of daylight
Surface = 0 m
Mixed layer
A physical/ecological model is proposed to assess the time
dependent vertical variability of plankton and nutrients in
oligotrophic pelagic ecosystems: western Mediterranean
and subtropical NE Atlantic
Euphotic
layer
• Blanes is 1DV , z-dependent
• Simulates vertical fluxes in
the upper 300 m of the water
column
N-stock
• Vertical
V ti l resolution
l ti = 3m
3
δz=3
Turbulent
Mi i
Mixing
(Kz ,Wz )
• Variable non-uniform
non uniform
vertical turbulent diffusion
(Osborn, 1980)
Bottom = 300 m
N-Input N-Output
• Depth-uniform (0.05 m/d)
upward vertical velocity
Physical components
A vertical
ti l tturbulent
b l t diff
diffusion
i model
d l
σθ(kg m-3)
28.0
0m
100 m
K (m2 s-1)
N (s-1)
10-33
29.0
-2
2
10
1
10
-3
10
Typical summer
stratification
Mixed
layer
Pycnocline
N 2(Z) = −
200 m
Water
Stability
(BruntVaisala)
K(z) =
g ∂ρ
•
ρw ∂ Z
ε(z)
0.25
N
2
Physical
p
components
Application
pp c o oof thee
vertical turbulent
diffusion model to
a subtropical
North Atlantic
section (above 500
m depth)
(Z)
300 m
Density
anomaly
10-8
ε
10-7
(m 2 s -3 )
TKE
Turbulent
diffusion
Osborn, 1980
Bahamón et al., 2003
Physical components :
Time evolution of daylight and PAR
Time evolution of irradiance
• The
Th B
Brock
k (1981) equations
ti
allow
ll th
the llength
th off d
daylight
li ht (L1)
to be computed according to latitude
⎛
⎡ ⎧ t + L1 − 12 ⎫⎤ ⎞
PAR(0, t) = PAR(0)⎜⎜1 + cos ⎢2π⎨
⎬⎥ ⎟⎟
L1
⎭⎦ ⎠
⎣ ⎩
⎝
500
PAR (W
Watts m -2)
N ⎞
⎛
PAR(0) = P0 + P1 sin ⎜ 2π
⎟
⎝ 365 ⎠
PAR (Watts m-22)
18
Daylightt (Hours)
• Time variation of PAR in surface:
D li h (h
Daylight
(hours))
15
12
9
400
300
200
100
6
0
2000
4000
6000
Time (Hours)
8000
0
2000
4000
6000
Time (Hours)
Subtropical NE Atlantic
Catalan Sea
The depth variation of PAR
- ⎛⎜⎜ kw +kc ∗ ⎡⎢⎣ PHY(i) ⎤⎥⎦∗ D ⎞⎟⎟
z⎠
PAR(i,
( t)) = PAR(i
( - 1,t)) exp ⎝
100 %
Deptth
Light extinction in sea water
Blanes Canyon head - NW Mediterranean, 2002
Water extinction
1.0 %
+
Phytoplankton selfshading
0.1%
8000
The biological
g
model fuelling
g
VOS 2
The upward diffusive flux of nitrogen (μmol m-2 s-1) results
from the diffusivity (Kz) multiplied by the nitrate gradient:
3
5
4
6
9
8
7
10
-50
Depth
-100
-150
Validation
of temperature
((°C)
C) simulations
in the
-200
⎡ ∂N ⎤
N flux = K z ⎢
∂z ⎥⎦
⎣ ∂z
-250
Field data
306
333 343
13
30
73
41
103
119
Algerian Sea
The new production deduced from the Redfield ratio:
16 mol of nitrate = 106 mol of carbon
Depth (m)
-50
-100
-150
-200
-250
Model results
300
330
360
30
60
120
90
Time (days)
Biological components and interactions
VOS 2
3
5
4
6
9
8
7
10
A simplified nitrogen cycling conceptual model
-50
Depth (m)
-100
-150
NH4+ - N
-200
-250
Validation of
temperature (°C)
simulations in the
306
13
30
73
41
103
119
Phytoplankton - N
Sinking
-100
Depth (m)
F l pellets,
Fecal
ll t
deaths
Uptake
p
-50
C l Sea
Catalan
S
Zooplankton
p
-N
Grazing
Uptake
Field data
333 343
Excretion
NO2- - N
-150
Exudation
NO3- - N
-200
-250
300
Model results
330
360
30
Time (days)
60
90
120
Upward
transport
Best fitting parameters and coefficients
The evolution equation
q
of N-phytoplankton
p y p
(PHY)
(
)
¿Which p
parameters are best?
Symbol Value
KNO3
KNO2
KNH4
ψ
γ
VPHY
μ
∈
Ω
λ
Kg
Imax
0.9
0.8
0.7
1.5
0.025
3.0
01
0.1
80
20
30
1.68
1.2
Definition
Units
Half saturation constant for nitrate uptake
Half saturation constant for nitrite uptake
Half saturation constant for ammonium uptake
p
Ammonium inhibition parameter for nitrate and nitrite uptake
Phytoplankton exudation fraction of nitrite
Phytoplankton maximum growth rate
Zooplankton mortalit
mortality rate
Ammonium fraction of zooplankton excretion
Faecal pellets fraction of zooplankton excretion (detrital)
Zooplankton
oop a to assimilation
ass
at o eefficiency
c e cy
Zooplankton half saturation for ingestion
Zooplankton maximum ingestion rate
mmol N m-3
mmol N m-3
mmol N m-3
mmol N m-3
%
d-1
d-11
%
%
%
mmol N m-3
d-1
∂PHY
∂
=
∂t
∂z
(
)
∂PHY ⎤
∂PHY
⎡
⎢⎣ K z ∂z ⎥⎦ − w + w s ∂z +
(
+ PHY U
NO3
+U
NO2
+U
NH4
)− PHY EXU − G
NH4+ - N
Excretion
Zooplankton - N
Grazing
Uptake
Phytoplankton - N
Fecal pellets,
deaths
Uptake
Sinking
NO2- - N
Sensitivity analysis would give an insight on the effect of changing
parameters on model simulations
Some model interactions
The phytoplankton uptake of nutrients (UNO3) is as follows:
Uptake of nitrate:
U NO3 = VPHY
NO3
e Ψ (NH4)
K NO3 + NO3
Uptake of nitrite
U NO2 = V PHY
NO2
eΨ(NH4)
K NO2 + NO2
Uptake of ammonia
NH4
U NH4 = VPHY
K NO4 + NH4
Exudation
NO3- - N
Upward
transport
BLANES (model) run-time display
Seasonal validation of N-phytoplankton (mmol m-3)
in the Catalan Sea
Simulations of N-phytoplankton
p y p
((mmol m-3)
1.0
0.9
0.8
-100
0
0.6
0.5
-50
0.4
-200
0.3
Catalan Sea
-300
0
0
0.7
60
120
180
240
Depth (m
m)
Depth ((m)
0
0.2
0
2
0.1
300
Depth (m)
0.5
-150
-200
-250
0.4
-100
-100
-300
winter
0.3
spring
autumn
summer
-350
350
0.3
-200
Subtropical North Atlantic
-300
0
0.0
120
180
240
1.0
0.0
0.5
1.0
0.0
0.5
1.0
0.0
0.5
1.0
Lines indicate model results.
results Points indicate field observations
0.1
60
0.5
0.2
300
The evolution equations of nutrients:
NH4+ - N
Excretion
i
Zooplankton - N
Grazing
Uptake
Fecal pellets,
deaths
Phytoplankton - N
Uptake
Sinking
--
NO2 N
Exudation
N - Nitrate:
∂NO3 ∂ ⎡ ∂NO3 ⎤
∂NO3
= ⎢K z
w
−
− U NO3 ∗ PHY
∂t
∂z ⎣
∂z ⎥⎦
∂z
N - Nitrite:
∂NO2 ∂ ⎡ ∂NO2 ⎤
∂NO2
−w
+ PHYEXU − U NO2 ∗ PHY
= ⎢K z
⎥
∂z
∂t
∂z ⎣
∂z ⎦
N - Ammonia:
55
∂NH4 ∂ ⎡ ∂NH4 ⎤
∂NH4
−w
= ⎢K z
+ ∈ −U NH4 ∗ PHY
⎥
∂t
∂z
∂z ⎣
∂z ⎦
NO3-
-N
Upward
transport
Simulations of N-nitrate ((mmol
m-3)
0
Seasonal validation of N-nitrate (mmol m-3) in the
Catalan Sea
8
0
6
-50
5
4
3
-200
2
Catalan Sea
-300
0
0
60
120
180
240
1
Depth (m)
Depth (m)
7
-100
-100
-150
-200
300
4.0
.0
-250
250
Depth
h (m)
3.5
3.0
-100
2.5
-300
summer
spring
winter
autumn
-350
2 0
2.0
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8 0
2
4
6
8
1.5
-200
1.0
Subtropical North Atlantic
-300
300
0
60
120
180
240
0.5
Lines indicate model results and points indicate field observations
300
Simulations of N-nitrite ((mmol m-3)
0
Seasonal validation of N-nitrite (mmol m-3) in the
Catalan Sea
0.7
0
0.5
0.4
0.3
-200
0.2
C t l Sea
Catalan
S
-300
0
0
-50
60
120
180
240
0 1
0.1
300
0.45
Depth (m
m)
Depth (m
m)
0.6
-100
-100
-150
-200
Deepth (m)
0 40
0.40
0.35
-100
-250
0.30
0.25
-300
winter
0.20
-200
0.15
0.10
-300
0
Subtropical North Atlantic
60
120
180
240
300
spring
p g
summer
autumn
-350
0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.4
0.05
0.00
Lines indicate model results and points indicate field observations
Algunas ventajas de la modelación numérica
Model products
p
Estimates of vertical nitrogen
g fluxes upwardthe
p
euphotic
p
zone
Depth
Advective fluxes Diffusive fluxes
m
-22
-11
µmol m d
-22
-11
µmol m d
• Validar hipótesis sobre elementos que forzan el
((eco)) sistema
• Simular flujos realistas de cuencas oceánicas y
topografía del fondo. Se pueden simular
(
(predecir)
d i ) ffuturos
t
escenarios
i a nivel
i l llocal,
l
regional, global.
• Interpolar información dispersa de barcos
barcos, boyas,
boyas
satélites
Total fluxes
-2
2
-1
1
µmol m d
-2
2
-1
1
mol m y
Catalan Sea
120 - 130
190 - 200
290 -300
144
298
380
1632
140
5
1776
438
385
0.64
0.16
0.14
582
197
5
606
317
192
0.22
0 11
0.11
0.07
Subtropical NE Atlantic
150 - 160
190 - 200
290 - 300
24
120
187
Algunas desventajas de la simulación numérica
• La simulación
ó no es fiel reflejo de la realidad.
• Muchas posibles fuentes de error: condiciones
i i i l
iniciales,
códigos
ódi
ffuente
t (b
(bugs),
) cálculo
ál l d
de lla
difusión turbulenta, supuestos…etc.
– Las ecuaciones algebraicas, esenciales en los códigos de los
programas, son ecuaciones discretas o aproximaciones
algebraicas
l b i
de
d llas ecuaciones
i
dif
diferenciales
i l (grid
( id approx.).
)
Referencias onon-line
•
Numerical Modelling
g Theory
y
http://www.physics.uq.edu.au/xmds/documentation/html/node65.html
•
Introduction to physical oceanography. Robert Steward.
Free web-based text book (and pdf) in physical oceanography and a chapter in numerical
modelling
http://www-ocean.tamu.edu/education/oceanworldold/resources/ocng_textbook/contents.html
•
Reference hidrodynamic model: Princeton Ocean Model (POM)
http://www.aos.princeton.edu/WWWPUBLIC/htdocs.pom/
prácticos deben ser más simples
p
q
que el sistema
– Los modelos p
real
•
List of coastal models
http://www.scisoftware.com/environmental_software/referral.php
http://woodshole.er.usgs.gov/operations/modeling/ecomsi.html
http://www.ebmtools.org/
•
….
¿Dónde publicar o conseguir información
sobre dinámica y modelado marino?
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Ecological modelling
Continental Shelf Research
Deep Sea Research
Dynamics of Atmospheres & Oceans
Encyclopedia of Ocean Sciences
Geophysical Research Letters
Journal of Atmospheric & Oceanic Technology
Journal of Geophysical Research
Journal of Marine Research
Journal of Marine Systems
Journal of Physical Oceanography
Ocean Dynamics
Ocean Modeling
Oceanography
Physics Today
Progress in Oceanography
Nature, Science, Tellus
PLoS (Public Library of Science)
…