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)

Hetero-trophs

Bacteria

Meso-Micro-

Particulates

Dissolved

Phytoplankton

Consumers

Pico-f

DiatomsFlagell-ates

NO3

PO4

NH4

Si

DIC

Nutrients

Cocco-liths

Meio-benthos

AnaerobicBacteria

AerobicBacteria

DepositFeeders

SuspensionFeeders

Detritus

NutrIents

OxygenatedLayer

Reduced Layer

RedoxDiscontinuity

Layer

AtmosphereO2 CO2 DMS

3D

IrradiationWind Stress

Heat Flux

0D

Cloud Cover

Riv

ers

and

boun

darie

s

1D

Forcing

Marine System Model: ERSEM

Ecosystem

Physics

GOTM

POLCOMS

UK

MO

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.

Shelf seas ecosystem hindcast – forecast modelling

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

7km Western ChannelPOLCOMS-ERSEMPML-delayed 7 day Hindcast 2002-pres

T, S, U, VT, S, U, V

T, S, U, VERSEM

Met Forcing NWP

POL/PML hindcast 1988/89

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:

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

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

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.

Model –Data Misfit

Model –Data Misfit

Model –Data Misfit

Assessment of overall model performance.

Taylor plot - L4

0

1

2

3

4

5

6

7

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

r2

σD

/σM

T(surface)

Dinoflagellates

S (surface)

PicophytoplanktonDiatoms

Chlorophyll

S (dmean) Flagellates

Bacteria

Silicate Phosphate Nitrate T (dmean)

100*

D

MDPbias

2

2

1

DD

MDME

- 13

- 11

- 9

- 7

- 5

- 3

- 1

1

Temp

(dmea

n)

Temp

(sur

f)

Sal (

dmea

n)

Sal (

surf)

Chl

Phos Nit Si

Diat

Flag

Dino

Phyt

Picos

Bacter

ia

Mod

el E

ffci

ency

- 125

- 100

- 75

- 50

- 25

0

25

50

75

100

125

Temp

(dmea

n)

Temp

(sur

f)

Sal (

dmea

n)

Sal (

surf

)Ch

lPh

os Nit SiDi

atFlag

Dino

Phyt

Picos

Bact

eria

% B

ias

Phytoplankton Seasonal Succession

L4 Climatology

L4 Climatology

Mesozooplankton

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 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 =1.

MDS

seasonW03Sp03S03A03W04Sp04S04A04

2D Stress: 0.16

seasonW03Sp03S03A03W04Sp04S04A04

2D Stress: 0.1

Data

Model

MDS constructed from temperature, salinity, chlorophyll, nitrate, phosphate silicate, diatom biomass, flagellate biomass, dinoflagellate biomass.

RELATE TEST

= 0.44, p=0.0001

T,S and Nutrients only= 0.55, p = 0.0001

Correlations between variables at L4

model tempm n1p mn3n mn4n m n5s mchl mp1c mp2c mp3c mp4ctm n1p -0.457847mn3n -0.570095 0.954959mn4n 0.046861 0.529493 -0.114804m n5s -0.402593 0.906356 0.905404 0.280813mchl 0.130871 -0.698287 -0.614281 -0.183459 -0.795268mp1c -0.303375 -0.055811 -0.197708 0.115604 -0.200407 0.372556mp2c 0.359215 -0.766679 -0.809386 -0.260506 -0.773454 0.735123 0.480541mp3c 0.245126 -0.605957 -0.600442 -0.005784 -0.697641 0.405182 0.054098 0.485834mp4c 0.24224 -0.634571 -0.7684 0.119805 -0.734496 0.734557 0.597562 0.832742 0.552918

temp n1p n3n n4n n5s chl p1c p2c p3c p4ctempn1p -0.337629n3n -0.465216 0.396891n4n 0.546979 0.140648 -0.537803n5s -0.051787 0.299217 0.634643 -0.090878chl -0.106652 -0.03518 -0.328287 -0.054339 0.024203p1c -0.127074 -0.033792 -0.262189 -0.06749 0.111345 0.887541p2c -0.218995 0.006721 -0.196063 0.08752 -0.104425 0.175688 0.02293p3c 0.212952 -0.467903 -0.461161 -0.078324 -0.425008 0.119782 0.016338 -0.049943p4c 0.084967 -0.424347 -0.508357 -0.069027 -0.368589 0.511074 0.425981 0.148858 0.847533

RELATE test between these data sets indicates a statistically significant similarity between the matrices = 0.53 p=0.012

i.e. model explains ~ 28% of observed correlations

Nitrate control in model to strong?

Model

Data

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 modela) Salinity and hence water column structure /

turbulenceb) Grazing pressurec) Nitrogen dynamics in phytoplanktond) Dinoflagellate dydnamic incorrect (lack of

motility / heterotrophy?)e) Optics

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:10.1016/j.jmarsys.2006.08.001.

Qualitative Validation

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.

Zooplankton species geographical shift

Resolving shifts in species distributions

We need to be able to model this to understand how climate will affect marine bioresources

• 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.

Summary seasonal cycles

Total Phytoplankton

Total Copepods

Spatial Distribution of ErrorsTotal Phytoplankton

% Model results month by month that differ from the CPR samples by less that 0.5 SD from the mean in 1988

• 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.

Summary

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.

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.

A coherent ecosystem approach.

Web based data delivery systems

In-Situ DataEarth Observation

Meteorological Station

3D Ecosystem Modelling