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On the use of long term observations for evaluating a shelf sea ecosystem model

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The Continuous Plankton Survey. Icarus Allen (PML) ... plankton abundance ... There are, however, differences in the timing of patterns in plankton seasonality. ... – PowerPoint PPT presentation

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Title: On the use of long term observations for evaluating a shelf sea ecosystem model


1
On the use of long term observations for
evaluating a shelf sea ecosystem model
  • Examples from the
  • The Western Channel Coastal Observatory
  • The Continuous Plankton Survey
  • Icarus Allen (PML),
  • Katy Lewis (PML), Jason Holt (POL), John Siddorn
    (Met Office), Anthony Richardson (SAHFOS/CISRO)

2
Marine System Model ERSEM
ERSEM - key features Carbon based process
model Functional group approach Resolves
microbial loop and POM/DOM dynamics Complex
suite of nutrients Includes benthic
system Explicit decoupled cycling of C, N, P, Si
and Chl. Adaptable DMS, CO2/pH, phytobenthos,
HABs. Consequently flexible and applicable to a
wide range of global ecosystems.
Ecosystem
Forcing
Cloud Cover
Wind Stress
Irradiation
Heat Flux
Physics
0D
Rivers and boundaries
1D
3D
UK MO
GOTM POLCOMS
3
Shelf seas ecosystem hindcast forecast modelling
Met Forcing NWP
Met Office 1/3o Atlantic FOAM model
Met Office POLCOMS 12 km Atlantic Margin Model
7km MRCS POLCOMS-ERSEM Met Office 7 day hindcast
2002-pres
T, S, U, V
T, S, U, V
POL/PML hindcast 1988/89
T, S, U, V ERSEM
7km Western Channel POLCOMS-ERSEM PML-delayed 7
day Hindcast 2002-pres
4
Western Channel Coastal Observatory
  • Overall Aims and Purpose
  • Our purpose is to integrate in situ
    measurements made at stations L4 and E1 in the
    western English Channel with ecosystem modelling
    studies and Earth observation.
  • 1. What is the current state of the ecosystem?
  • 2. How has the ecosystem changed?
  • 3. Short term forecasts of the state of the
    ecosystem.
  • 4. The WCO as a National Facility for EO
    algorithm development, calibration and
    validation

5
Western Channel Coastal Observatory
  • Western English Channel
  • boundary region between oceanic and neritic
    waters
  • straddles biogeographical provinces
  • both boreal / cold temperate
  • warm temperate organisms
  • considerable fluctuation of flora and fauna
    since records began.
  • Southward et al. (2005) Adv. Mar. Biol., 47

6
Station L4
  • Situated 10nm south of Plymouth
  • Sampled weekly for physical, biological and
    chemical data since 1992.
  • Hydrodynamically complex
  • Average depth of 50m
  • Classified as a well-mixed tidal station but it
    exhibits weak seasonal stratification in summer
    and is influenced by the outflow from the River
    Tamar.
  • On some occasions it represents the margin of the
    tidal front characteristic of this region
    (Pingree, 1978).

Complex system so a good test of the model
dynamics
7
Station L4
  • The thermohaline structure of the water column
    was determined with a CTD probe developed from
    the Undulating Oceanographic Recorder (UOR)
    (Aiken Bellan, 1990).
  • Water samples (10m depth) analysed for nitrate,
    phosphate and silicate concentrations using
    standard laboratory colorimetric methods
    (Woodward Rees, 2001).
  • Chlorophyll-a concentrations, fluorometric
    analysis with a Turner Design 1000R fluorometer
    after extraction in 90 acetone overnight.
    (Rodriguez et al., 2000)
  • Phytoplankton is collected at 10m depth and
    preserved with 2 Lugols iodine solution
    (Holligan Harbour, 1977).
  • Between 10 and 100ml of sample, depending on cell
    density, were settled and species abundance was
    determined using an inverted microscope.
  • Cell volume and carbon estimates for the
    microplankton were derived from the volume
    calculations of Kovala Larrance (1966) and the
    cell volume and carbon estimations of Eppley et
    al. (1970).
  • Zooplankton samples are collected by vertical net
    hauls (WP2 net, mesh 200µm UNESCO, 1968) from
    the sea floor to the surface and stored in 5
    formalin.
  • (Bacteria and picophytoplankton (the combination
    of synecoccus bacteria and picoeukaryotes))
    determined using a flow cytometer.

8
Model Data Misfit
9
Model Data Misfit
10
Model Data Misfit
11
Assessment of overall model performance.
12
(No Transcript)
13
Phytoplankton Seasonal Succession
14
L4 Climatology
15
L4 Climatology
16
Mesozooplankton
17
Multivariate Analysisall analysis's performed
using PRIMER 6
  • MDS (multi-dimensional scaling)
  • Cluster analysis allows us to check the
    adequacy and mutual consistency of both the model
    and the in-situ data.
  • A multi-variate ordination technique which
    can be used to reflect configurations in the
    model and in-situ data
  • A non-metric MDS algorithm constructs MDS
    plots iteratively by as closely as is possible
    satisfying the dissimilarity between samples
    dissimilarities between pairs of samples, derived
    from normalised Euclidean-distance matrices, are
    turned into distances between sample locations on
    a map.
  • RELATE Test.
  • A test of no relationship between distance
    matrices, essentially a test for concordance in
    multivariate pattern.
  • A correlation between corresponding
    elements in each distance matrix was calculated
    using Spearmans rank correlation, adjusted for
    ties (Kendall, 1970).
  • The significance of the correlation was
    determined by a Monte Carlo permutation
    procedure, using the PRIMER program RELATE.
  • For the ideal model r 1.

18
MDS
MDS constructed from temperature, salinity,
chlorophyll, nitrate, phosphate silicate, diatom
biomass, flagellate biomass, dinoflagellate
biomass.
Data
RELATE TEST r 0.44, p0.0001 T,S and
Nutrients only r 0.55, p 0.0001
Model
19
Correlations between variables at L4
Model
Nitrate control in model to strong?
Data
RELATE test between these data sets indicates a
statistically significant similarity between the
matrices r 0.53 p0.012 i.e. model explains
28 of observed correlations
20
Summary
  • Model does well reproducing temperature and has
    some skill for nutrients, but phytoplankton must
    be improved before any confidence can be had in
    the model ability to forecast.
  • The model does not accurately simulate the timing
    of the spring bloom and further work is required
    to assess whether the causes of this are
    hydrodynamic, optical or physiological.
  • Issues with model
  • Salinity and hence water column structure /
    turbulence
  • b) Grazing pressure
  • c) Nitrogen dynamics in phytoplankton
  • d) Dinoflagellate dydnamic incorrect (lack of
    motility / heterotrophy?)
  • e) Optics

21
Qualitative Validation
  • Model validation with
  • plankton abundance

K. Lewis et al., Error quantification of a high
resolution coupled hydrodynamic-ecosystem
coastal-ocean model Part3, validation with
Continuous Plankton Recorder data, Journal of
Marine Systems (2006), doi10.1016/j.jmarsys.2006.
08.001.
22
Continuous Plankton Surveywww.sahfos.ac.uk
The aim of the CPR Survey is to monitor the
near-surface plankton of the North Atlantic and
North Sea on a monthly basis, using Continuous
Plankton Recorders on a network of shipping
routes that cover the area.
23
Resolving shifts in species distributions
Zooplankton species geographical shift
We need to be able to model this to understand
how climate will affect marine bioresources
24
  • Simulated tows were performed by extracting
    biomass data from archived model
  • Due to the semi-quantitative nature of the CPR,
    data for each individual tow of both the CPR and
    corresponding model output were standardised to a
    mean of zero and a unit standard deviation (s) of
    the relevant data to produce a dimensionless
    z-score.
  • This allows a direct qualitative comparison of
    model biomass with discrete survey counts.

Domain-wide daily mean values for all CPR samples
and corresponding model output were used to
compare the magnitude and timing of the behaviour
of the biological variables over the two-year
period.
25
Summary seasonal cycles
26
Total Phytoplankton
27
Total Copepods
28
Spatial Distribution of Errors Total Phytoplankton
29
Model results month by month that differ from
the CPR samples by less that 0.5 SD from the mean
in 1988
30
Summary
  • Simple linear regression and absolute error maps
    provide a qualitative evaluation of
    spatio-temporal model performance
  • z-scores indicate model reproduces the main
    pelagic seasonal features
  • good correlation between magnitudes of these
    features with respect to standard deviations from
    a long-term mean.
  • The model is replicating up to 62 of the
    mesozooplankton seasonality across the domain,
    with variable results for the phytoplankton.
  • There are, however, differences in the timing of
    patterns in plankton seasonality.
  • The spring diatom bloom in the model is too
    early, suggesting the need to reparameterise the
    response of phytoplankton to changing light
    levels in the model.
  • Errors in the north and west of the domain imply
    that model turbulence and vertical density
    structure need to be improved to more accurately
    capture plankton dynamics.

31
General Conclusions
  • Long-term time series observations are important
    resources for the assessment of model
    performance they can be used to highlight errors
    in model hindcasts, which can subsequently be
    improved.
  • These types of analysis are only possible because
    of the existence of large self-consistent data
    sets. Unfortunately, such data sets are
    relatively rare and a concerted effort is
    required to collate existing data sets into model
    friendly formats, collect new ones and make them
    readily available.
  • L4 is situated in a hydrographically complex
    region therefore it provides a substantial test
    of model ability, however for the model to be
    evaluated more extensively it is essential to
    perform these tests over a wider spatial scale.

32
  • Advances in Marine Ecosystem Modelling Research
  • Workshop on validation of global ecosystem
    models (4-6th Feb 2007)
  • Workshop on Ocean Acidifcation (11-13th Feb
    2007)
  • Both workshops to be held in Plymouth, register
    online at www.amemr.info by 17th November.
  • AMEMR II is scheduled for June 2008.

33
A coherent ecosystem approach.
In-Situ Data
Earth Observation
Meteorological Station
3D Ecosystem Modelling
Web based data delivery systems
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