on the use of long term observations for evaluating a shelf sea ecosystem model examples from the...
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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.
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
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.
% 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.