Title: On the use of long term observations for evaluating a shelf sea ecosystem model
1On 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)
2Marine 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
3Shelf 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
4Western 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
5Western 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
6Station 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
7Station 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.
8Model Data Misfit
9Model Data Misfit
10Model Data Misfit
11Assessment of overall model performance.
12(No Transcript)
13Phytoplankton Seasonal Succession
14L4 Climatology
15L4 Climatology
16Mesozooplankton
17Multivariate 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.
18MDS
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
19Correlations 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
20Summary
- 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
21Qualitative 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.
22Continuous 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.
23Resolving 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.
25Summary seasonal cycles
26Total Phytoplankton
27Total Copepods
28Spatial 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
30Summary
- 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.
31General 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.
33A coherent ecosystem approach.
In-Situ Data
Earth Observation
Meteorological Station
3D Ecosystem Modelling
Web based data delivery systems