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.
Forcing
Ecosystem
Hetero-
trophs
Bacteria
Meso-
Micro-
Particulates
Dissolved
Phytoplankton
Consumers
Pico-f
Diatoms
Flagell
-ates
NO3
PO4
NH4 Si
DIC N u t r i e n s
Cocco-
liths
Meio-
benthos
Anaerobic
Aerobic
Deposit
Feeders
Suspension D I
Oxygenated
Layer
Reduced
Redox
Discontinuity
Atmosphere O2
CO2
DMS
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% Lugol’s 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.

13. Phytoplankton Seasonal Succession

14. L4 Climatology

15. L4 Climatology

16. Mesozooplankton

17. Multivariate Analysis all 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 Spearman’s 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 Data RELATE TEST r = 0.44, p=0.0001 T,S and Nutrients only
MDS constructed from temperature, salinity, chlorophyll, nitrate, phosphate silicate, diatom biomass, flagellate biomass, dinoflagellate biomass.
Data
RELATE TEST
r = 0.44, p=0.0001
T,S and Nutrients only
r = 0.55, p =
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 p=0.012
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), doi: /j.jmarsys

22. Continuous Plankton Survey www.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. in species distributions
Resolving shifts
in species distributions
Zooplankton species
geographical shift
Second issue is resolving the trophic food web How much detail do we need to resolve?
A sketch showing the feeding relations between herring of different ages and the plankton for the North Sea, redrawn from (S1). The arrows point in the direction of predation.
The complexity of this incomplete food web, for a limited region, illustrates the challenge of representing the ecosystem connections even for a single species.
Note in particular the reversal of arrows between herring and plankton that occurs as herring grow larger. Some plankton (e.g. Sagitta) are predators of juvenile herring (dashed line) but are prey to adult herring (solid line).
The biological detail of most models increases with trophic level, probably because there is more knowledge of the larger, more easily handled organisms.
However, this structure is not optimal, and it risks artifacts arising from competitive interactions implied by the lack of food web resolution in the lower trophic levels.
Even a simplified food web, such as for North Sea herring (fig. S1), cannot be completely simulated numerically because of the absence of data and the difficulty of parameterizing interactions.
Some simplification of the structure and predator-prey relations is invariably necessary; however, the traditional approach of representing trophic or functional groups of species by bulk biomass variables is not adequate for all trophic levels (17).
Taxa such as zooplankton and fish, where juveniles and adults differ in body size by many orders of magnitude and there is the potential for long-distance active migration, require a different approach that resolves the developmental changes in diet, physiology, and behavior.
Unfortunately, to account for climate change / inter-annual variability we need to resolve the two principle Calanus sps.
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 (σ) 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 by 17th November.
AMEMR II is scheduled for June 2008.

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