Transcript Slide 1
The Western (English) Channel Observatory as a test bed for improving ecosystem forecasts. ‘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) www.westernchannelobservatory.org.uk Icarus Allen, Tim Smyth et al. PML Sustained Observations in the Western English Channel: past, present and future. Plymouth time series since 1900 Geographical Region 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 theChannel: ecosystem changed? Western English • boundary region between oceanic and neritic waters; •Improve short term forecasts of the state of the ecosystem. • straddles biogeographical provinces; • both boreal / cold temperate & • A national facility for EO algorithm development, calibration and • warm temperate organisms validation. • considerable fluctuation of flora and fauna since records began. Southward et al. (2005) Adv. Mar. Biol., 47 Web (Webmap server) • each element has strengths and weaknesses – synergy. Database (SQL) Virtual Observatory MECN: Knowledge Transfer / policy advice Modelling Data ERSEM Met Office (NCOF) Remote Observatory In-situ sampling (L4, E1, L5, buoy, etc.) Remote Sensing long-term time-series (linked to non WCO series via MECN) SST, Ocean Colour scientific investigation Other sensors In-Situ Sampling 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) Latest Hydrography The year so far (L4) … L4 - Zooplankton time-series vertical net hauls: from the sea floor (~55m) to the surface WP2 net: mesh 200µm (UNESCO 1968) L4 samples are stored in 5% formalin taxonomic identification zooplankton database latitude: 50.67°N longitude: 4.58°W data analysis start: 1988 sample frequency: weekly abundance: 1988- on going biomass: 1993-1998 copepods eggs production: 1992-2005 Composition of the “Top20” dominant species at L4 (Plymouth) 19882006 Sagitta sp. 90% 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. PML:(base node) Met. Station; Aerosols; L4:Moored profiling buoy CTDf, Optics, Nutrients … expandable for visitors? E1:Moored (profiling?) buoy CTDf, Optics, Nutrients … Rame Head:(shore node) Met. Station; Aerosols; Remote Observatory Remote Sensing frontal region frontal region Tidal mixing Autumn bloom case I stratified • • SST (1981 - ) and ocean colour (1997 - ) to provide synoptic overview of WCO domain. Delivered via web and Web Map Server technology. case II 26 Aug - 01 Sep 16-22 Sep AVHRR SST 02-08 Sep 9-15 Sep 23-29 Sep NRT data provided in partnership with NEODAAS PML. MODIS chlorophyll-a 26 Aug - 01 Sep 9-15 Sep 02-08 Sep 16-22 Sep 23-29 Sep NRT data provided in partnership with NEODAAS PML. Virtual Observatory Western English Channel Model 7km Western Channel POLCOMS-ERSEM PML-delayed 7 day Hindcast 2002-pres 2km under-development, 500m regional submodel in vicinity of L4/E1 proposed Marine System Model: ERSEM Ecosystem Forcing Atmosphere Cloud Cover Wind Stress O2 CO2 DMS Irradiation Heat Flux Physics Pico-f Phytoplankton Coccoliths Flagell -ates Si NO3 Diatoms Particulates DIC NH4 Bacteria PO4 Rivers and boundaries 0D UK MO Dissolved 1D Heterotrophs Micro- Meso- Consumers Suspension Feeders 3D GOTM POLCOMS D e t r i t u s Oxygenated Layer Aerobic Bacteria Meiobenthos Anaerobic Bacteria Deposit Feeders Redox Discontinuity Layer Reduced Layer N u t r I e n t s N u t r i e n t s Development of Model Metrics Validation and verification Relationships between model and data (adapted from the ideas of Dan Lynch) Data x FN x Predictive x Error x x x t1 x Predictionx x TN x x x Observational Error Truth x T x x x TP x x xP O x FP Predictive t1 uncertainty (e.g. numerical error, parameter uncertainty) Dsicrimination Analysis Observation Residual f(O-P) Model Data assimilation is the art of reducing this distance Observational accuracy, Taylor (e.g.Diagram measurement error, range of replicates etc.) How well does ERSEM capture the seasonal succession at L4? 7 6 Multivariate Validation: Comparison of PC1 for modelled and observed phytoplankton Dinoflagellates Assessing short term forecast skill Like with Like comparison S (surface) 20 5 4 3 2 -20 PC1 σD/σM 0 -40 Log Chlorophyll -60 Monthly Mean Chlorophyll Chlorophyll 1 0 -80 S (dmean) Flagellates Silicate Diatoms Bacteria -100 Picophytoplankton 0 100 0.3200 0 0.1 0.2 Phosphate Nitrate T (dmean) T(surface) 0.4 300 0.5 4000.6 Day 2003-04 r2 500 0.7 600 0.8 L4 700 0.9 Model 1 Data Delivery Hosting of Ecosystem parameters (NCOF box) Summary If the model predicts a bloom what’s the probability it is correct? PPV CP CP IP Positive Predictive Value A. PPV for threshold > mean chlorophyll, B. PPV for threshold > 1.5 mean chlorophyll for that pixel, Allen et al Harmful Algae (in press)