The Western (English) Channel Observatory as a test bed for improving ecosystem forecasts.
Icarus Allen, Tim Smyth et al. PML
www.westernchannelobservatory.org.uk
‘Growing concern about human influence on marine ecosystems conflicts with our inability to separate man-made from ‘natural’ change. This limitation results from lack of adequate baselines and uncertainty as to whether observed changes are local or on a broad scale. Long-term monitoring programmes should be able to solve both these deficiencies’ (Duarte et al, 1992. Nature)
Sustained Observations in the Western English Channel: past, present and future.
Plymouth time series since 1900
Geographical Region
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
Overall aims and purpose Our purpose is to integrate in situ measurements made at stations L4, L5, E1 and adjacent coasts in the western English Channel (see Fig 3) with ecosystem modelling studies and Earth observation; this will be facilitated by web-based GIS technology. which allows the following science questions to be addressed at a range of temporal and spatial scales:
•What is the current state of the ecosystem?
•How has the ecosystem changed? •Improve short term forecasts of the state of the ecosystem.
• A national facility for EO algorithm development, calibration and validation.
Remote Observatory
Virtual Observatory
In-situ sampling (L4, E1, L5, buoy, etc.)
long-term time-series (linked to non WCO series via MECN)
scientific investigation
Remote Sensing
SST, Ocean Colour
Other sensors
Modelling
ERSEM
Met Office (NCOF)
Data
Database (SQL)
Web (Webmap server)• each element has strengths and weaknesses – synergy.
MECN: Knowledge Transfer / policy advice
In situ sampling
i) Marine measurements:
OPERATIONS: weekly sampling @ L4; fortnightly @ E1
• The Observatory consists of the core measurements:
• Hydrography (CTDf);
• Nutrients;
• Optics;
• Pigments;
• Zooplankton and phytoplankton
ii) Atmospheric measurements:
• meteorological stations (PML, Rame Head)
• sun photometric aerosol retrievals (PML)
latitude: 50.6750.67°°NNlongitude: 4.584.58°°WW
start: 19881988sample frequency: weeklyweekly
abundance: 1988- on going1988- on goingbiomass: 1993-19981993-1998copepods eggs production: copepods eggs production: 1992-20051992-2005
L4L4
L4 - Zooplankton time-seriesL4 - Zooplankton time-series
vertical net hauls: from the sea floor (~55m) to the surfaceWP2 net: mesh 200µm (UNESCO 1968)
samples are stored in 5% formalin
taxonomic identification
zooplankton database
data analysis
Array of autonomous moorings• An observatory needs to directly observe something;
• Need real-time data (rather than just RS / modelling);
• Capital bid successful: currently specifications out to tender …
• Have permissions for moorings at L4 and E1.
E1:Moored (profiling?) buoy
CTDf, Optics, Nutrients …
L4:Moored profiling buoy
CTDf, Optics, Nutrients … expandable for visitors?
Rame Head:(shore node)
Met. Station; Aerosols;
PML:(base node)
Met. Station; Aerosols;
Remote Sensing
• SST (1981 - ) and ocean colour (1997 - ) to provide synoptic overview of WCO domain.
• Delivered via web and Web Map Server technology.
Tidal mixing
frontal region
stratified
frontal region
case I
case IIAutumn bloom
AVHRR SST26 Aug - 01 Sep 02-08 Sep 9-15 Sep
16-22 Sep 23-29 Sep
NRT data provided in partnership with NEODAAS PML.
MODIS chlorophyll-a26 Aug - 01 Sep 02-08 Sep 9-15 Sep
16-22 Sep 23-29 Sep
NRT data provided in partnership with NEODAAS PML.
Western English Channel Model
7km Western Channel POLCOMS-ERSEMPML-delayed 7 day Hindcast 2002-pres
2km under-development, 500m regional sub-model in vicinity of L4/E1 proposed
Hetero-trophs
Bacteria
Meso-Micro-
Particulates
Dissolved
Phytoplankton
Consumers
Pico-fDiatoms
Flagell-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
Hetero-trophs
Bacteria
Meso-Micro-
Particulates
Dissolved
Phytoplankton
Consumers
Pico-fDiatoms
Flagell-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
Hetero-trophs
Bacteria
Meso-Micro-
Particulates
Dissolved
Phytoplankton
Consumers
Pico-fDiatoms
Flagell-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
TruthT
OResidualf(O-P)
Observation
Prediction
PredictiveError
Observational Error
Validation and verificationRelationships between model and data(adapted from the ideas of Dan Lynch)
Predictiveuncertainty(e.g. numerical error, parameter uncertainty)
Observationalaccuracy, (e.g. measurement error, range of replicates etc.)
Data assimilation isthe art of reducing this distance
P
TruthT
OResidualf(O-P)
Observation
Prediction
PredictiveError
Observational Error
Validation and verificationRelationships between model and data(adapted from the ideas of Dan Lynch)
Predictiveuncertainty(e.g. numerical error, parameter uncertainty)
Observationalaccuracy, (e.g. measurement error, range of replicates etc.)
Data assimilation isthe art of reducing this distance
P
Development of Model Metrics
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Model
Data
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t1
t1
TN
TP
FP
FN
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Model
Data
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t1
t1
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TP
FP
FN
Dsicrimination Analysis Taylor Diagram
How well does ERSEM capture the seasonal succession at L4?
-100
-80
-60
-40
-20
0
20
0 100 200 300 400 500 600 700
Day 2003-04
PC
1
L4 Model
Multivariate Validation: Comparison of PC1 for modelled and observed phytoplankton
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1
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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)
Assessing short term forecast skillLike with Like comparison
Log Chlorophyll
Monthly Mean Chlorophyll