Presentation on theme: "Sabrina Speich S. Russo, O. Aumont, E. Machu, C. Messager Institut Universitaire Européen de la Mer & LMI ICEMASA V. Garçon, B. Le Vu (LEGOS, Toulouse)"— Presentation transcript:
Sabrina Speich S. Russo, O. Aumont, E. Machu, C. Messager Institut Universitaire Européen de la Mer & LMI ICEMASA V. Garçon, B. Le Vu (LEGOS, Toulouse) Y. Shin (UMR EME & LMI ICEMASA) L. Shannon, C. Molooney (UCT, Afrique du Sud)
MEECE is a FP7 Integrated Project which aims to push forward the state-of- the-art of our understanding of impacts of global climate change and direct anthropogenic drivers on marine ecosystems end to end The specific goals of MEECE are: To improve the knowledge base on marine ecosystems and their response to climate and anthropogenic driving forces To develop innovative predictive management tools and strategies to resolve the dynamic interactions of the global change driver, changes in ocean circulation, climate, ocean acidification, pollution, over fishing and alien invasive species on the structure and functioning of marine ecosystems Coordinator: Icarus Allen Plymouth Marine Laboratory (PML), UK |
MEECE integrated ecosystem changes approach
Climate Global Models: underestimation of climate subsystem processes Global Climate Models not yet adequate to reproduce the whole spectra of atmospheric, oceanic and air-sea exchanges processes HadCM3 SST error (model-simulated) Emission scenarios
Modeling approaches to ‘downscaling’ from global to regional scale 1. using a regional climate model (RCM) – often referred to as ‘dynamical downscaling’. Note that this involves a two-step process, driving RCM at its boundaries by results from a GCM. 2. making use of empirical relationships between large and smaller scales based on historical observations – referred to as ‘statistical downscaling’. Note that this requires long-term and high- quality observations at the location/region in question. 3. using a ‘stretched grid’ global model, with high resolution over the domain of interest and lower resolution elsewhere. Note that this poses challenges for physical parameterizations, flow distortion, etc., but avoids problems at boundaries. 4. use global climate model to produce ‘high resolution time slices’. Note that this avoids boundary problems, but there may be issues with initial conditions, parameterizations, ocean boundary conditions, etc. 1. using a regional climate model (RCM) – often referred to as ‘dynamical downscaling’. Note that this involves a two-step process, driving RCM at its boundaries by results from a GCM. 2. making use of empirical relationships between large and smaller scales based on historical observations – referred to as ‘statistical downscaling’. Note that this requires long-term and high- quality observations at the location/region in question. 3. using a ‘stretched grid’ global model, with high resolution over the domain of interest and lower resolution elsewhere. Note that this poses challenges for physical parameterizations, flow distortion, etc., but avoids problems at boundaries. 4. use global climate model to produce ‘high resolution time slices’. Note that this avoids boundary problems, but there may be issues with initial conditions, parameterizations, ocean boundary conditions, etc. but climate predictions & projections must be done at global scale, because the system’s response is fundamentally global
First step: A dynamical downscaling of the ocean using the Regional Ocean Model System (ROMS) Climate Scenario Downscaling Dynamical downscaling runs regional (climate) models in reduced (regional) domain with boundary conditions given by the (AR4) GCMs Russo & Speich in prep.
Climate Scenario Downscaling Second step: A statistical calibration of the climate (IPSL A1B) scenario COADS Hyp.: Find an empirical function T that downscales (or corrects the model outputs) cumulative distribution function (CDF) of a climate variable from large- (the predictor) to local-scale (the predictand) by applying an equivalent of proportionality transformation 1 Russo & Speich in prep. 1 Michelangeli et al. 2009
SOUTHERN AFRICA 50°S 30°S 25°S 40°S 45°S 10°W010°E20°W30°W Climate Scenario Downscaling Future steps: 1.Improving the physical downscaling by using a coupled atmosphere-ocean regional system forced at boundaries by the statistically corrected AR4 (AR5) GCMs; 2.Adding the biogeochemistry components to the regional coupled system (NPZD, ecosystems, end- to-end models) 3.Implementing a full coupled regional system (including land biosphere, hydrology, atmosphere chemistry, etc.) ? WRF forced by OSTIA SST Latent Heat Flux
MEECE integrated ecosystem changes approach
Application to South Benguela for 11 explicit species ¾ fish biomass >90% of captures SOUTH AFRICA 0.15° x 0.15° OSMOSE Model (high trophical levels) in the Benguela Model dimensions: Abundance and Biomass by: Species Age Size Space unit Time unit Ratio max Ratio min Predator size Prey size 1.Min-Max limits for the size pred/prey ratio 2.Spatio-temporal co-occurrence Variable structure of the trophical network Opportunist predation: buffer role
Spatial distribution (x,y) 1 1 (x,y+1) (x+1,y+1) (x+1,y-1) (x,y-1)(x-1,y-1) (x-1,y) Processes Natural mortality 2 Explicit predation 3 Growth or Mortality by starving 4 Mortality by fishing 5 Reproduction 6 Ex : hareng Age 0 – sem 1 Ex : hareng Age 3+ OSMOSE: Modelling the life cycle
Forcing & Coupling: ROMS-NPZD-OSMOSE Travers et al Ecol. Model. Natural mortality Predation Growth Fishing mortality Reproduction ξ Small Detritus Copepods Flagellates Nitrates Large Detritus Ciliates Ammonium Diatoms Food availability (x,y,t,size) 1 1 One-way coupling = Forcing Starvation mortality 2 Predation mortality 2 Two-way coupling (feedback) = Coupling OSMOSE ROMS-NPZD - Parametrization Shin et al S. Afr. J. Mar. Sci. Travers et al Can. J. Fish. Aquat. Sci. - Sensibility analyses Ferrer 2008, Msc thesis - Calibration by genethic algorithm Versmisse 2008, PhD thesis Duboz et al Ecol. Model. -Validation – POM approach Travers 2010, PhD thesis - Crossed validation with Ecopath-Ecosim Shin et al S. Afr. J. Mar. Sci. Travers et al., J. Mar. Sys. ROMS-NPZD coupling Travers et al., Ecol. Modelling Travers et Shin, Progress Oceanogr
Scenarios with fishing and climate variability Y. Shin (UMR EME), L. Shannon (UCT) Three questions will be addressed in the Benguela, using Roms-Npzd-Osmose and EwE: 1.How would climate change affects fishing reference levels? 2.Would climate change and fishing scenarios modify the trophic structure of the ecosystem? 3.To what extent are ecological indicators of fishing effects sensitive and exclusive to fishing pressure (vs sensitive to climate forcing)? MEECE integrated approach on the Benguela ecosystem Climate variability and impact S. Speich, S. Russo, E. Machu, O. Aumont, C. Messager (LPO IUEM), V. Garçon, B. Le Vu (LEGOS), Y. Shin (UMR EME), C. Mooloney (UCT) Two questions adressed: 1.How climate change impacts the regional climate system ? 2.How this affects the local ecosystems (adresses via different coupled systems: ROMS-NPZD-OSMOSE et ROMS-PISCES-APECOSM) Would climate change and fishing scenarios modify the trophic structure of the ecosystem? Shift between different alternative trophic pathways?
Comparing ecological indicators across world’s marine ecosystems the IndiSeas Working Group OBJECTIVES The IndiSeas WG was established in 2005 under the auspices of EUROCEANS to: Develop a set of synthetic ecological indicators; Build a generic dashboard using a common set of interpretation and visualisation methods; Evaluate the exploitation status of marine ecosystems in a comparative framework A suite of papers published in ICES Journal of Marine Science (2010) presents initial results of comparative analyses of the 19 fished marine ecosystems (Shin and Shannon 2010; Shin et al. 2010a). In blue, the first 19 ecosystems considered in the IndiSeas WG. In yellow, the participating countries The IndiSeas WG relies strongly on a multi-institutional collaboration for assembling a common dataset, and for allowing the global comparative approach to keep a good track of the data which underlie the indicators, and to account for the local scientific knowledge in the final diagnosis. The first phase of the WG ( ) assembled the expertise of 31 scientific experts around the world, from 21 research institut IndicatorsHeadline label Mean lengthFish size Trophic level of landingsTrophic level Proportion of under to moderately exploited species % Healthy stocks Proportion of predatory fish% Predators Mean life spanLife span 1/CV of total biomassBiomass stability Total biomass of surveyed species Biomass Biomass:LandingsInverse fishing pressure Yunne-Jai SHIN IRD, UMR EME 212 Lynne SHANNON UCT, Zoology Dpt
Comparing ecological indicators across world’s marine ecosystems Next steps Building bridges with other scientific fields To strengthen the ecosystem diagnosis, additional indicators from other scientific fields need to be considered, allowing to: Quantify the joint effects of climate and fishing changes Integrate conservation and biodiversity issues Integrate socio-economic issues Testing the performance of ecosystem indicators in fisheries management Performance testing will allow to assess whether an indicator and accompanying decision rules actually guide decision-makers to make the “right” decision, in hindsight. The suite of indicators collected by the Indiseas WG provides a unique opportunity to test their performance across a range of ecosystems. Developing reference levels for indicators Establishing reference levels for ecosystem indicators has proven to be a major challenge to implementing EAF, due to the complexity of ecosystems and their response to fishing in a changing environment. Ecosystem models (EwE, Osmose, Atlantis) will be used for identifying baseline unexploited reference levels and limit reference levels. For each ecosystem, a synthetic overview is displayed with state and trends indicators. A summary diagnosis is provided by each ecosystem expert. Viewing options include time series for each indicator, descriptions of ecosystem and key species. T he IndiSeas website The website has been developed as a platform to disseminate the results of the analyses beyond the scientific audience. It is intended to inform scientists, managers, policy makers and the public at large of the state of the world’s marine ecosystems as a result of fisheries exploitation. Yunne-Jai SHIN IRD, UMR EME 212 Lynne SHANNON UCT, Zoology Dpt
Spatio-temporal variation of fish- induced mortality on plankton Predation mortality rate on copepods (d -1 ) Forçage/couplage ROMS-NPZD et OSMOSE Travers et Shin Progr.Ocean Predation mortality rate on copepods (day -1 ) Travers et al Ecol. Model. ForçageCouplage Biomasse (t) Diatomées Couplage = moins de plancton dans la zone de nourricerie Quel est l’effet de la rétroaction?
- Des scénarios d’Aires Marines Protégées (ANR AMPED, coord. D. Kaplan) Avec Y. Shin (UMR EME), D. Yemane (MCM), C. van Der Lingen (MCM), N. Bez (IRD) Deux effets à tester avec ROMS-NPZD-OSMOSE: 1- Variabilité spatiale des réseaux trophiques 2- Changements d’habitats des espèces exploitées (scénarios IPCC) Vers des scénarios prospectifs dans le Benguela sud Hutchings et al Life-history migration Weeks et al Agulhas current Benguela current The same species occur in the South and West coasts so many interactions between the 2 zones
1) How would climate change affects fishing reference levels? - Simulate F MSY present conditions (already done in MSC LTLWG – T. Smith), and compare with simulations under IPCC scenarios (at least A1B, time slice ) - For a set of key target species (monospecies approach). In the Benguela: anchovy, sardine, redeye, horse mackerel, shallow water hake, deep water hake
2) Would climate change and fishing scenarios modify the trophic structure of the ecosystem? Shift between different alternative trophic pathways? Combined fishing and IPCC scenarios. 4 fishing scenarios: - F status quo - Increase in F(global), F(small pelagics), F(large demersals)
2) Sensitivity and responsiveness of ecological indicators to fishing vs climate forcing indicator Fishing mortality F ? Linear decrease ? ? Environmental noise ? Theoretical climate and fishing forcing: - Implement present climate conditions, increase in wind stress (trend), interannual variability - multiplier of F(global) - F(small pelagics): 0 to Fdepletion - F(demersal fish): 0 to Fdepletion Set of indicators to be tested: Mean size of fish, proportion of predatory fish, mean lifespan, 1/CV tot biomass, tot B, TL landings