Development of the MOHID consortium

1. Development of the MOHID consortium Presentation of 4 PhD thesis works

2. Menu Thesis menu • High resolution tridimensional hydrodynamical model of the Portuguese coast (Guillaume Riflet) • Operational modelling in Estuaries (Angela Canas) • Bidirectional coupling of atmospheric and oceanic regional circulation models (Ana Rosa Trancoso) • Climate change, fisheries pressure and coastal upwelling ecosystems (Ricardo Lemos)

3. Guillaume Thesis main goals • Development of a high resolution hydrodynamical 3D model of the Portuguese coast with MOHID. • Coupling the model to a large scale Atlantic ocean model (ex: HYCOM, MERCATOR, MOHID). Nested models • Use of remote-detection surface data (buoys and satellite temperature surface and altimetry data) to validate the model’s results.

4. 3 nested levels: Atlantic scale, regional scale and local scale. Implementation and testing of several vertical discretization schemes (sigma, cartesian, hybrid). Definition of initial condition from objective analysis of available datasets. Creation of coherent and stable climatologic fields (ex:Levitus?). Quantification of the pressure gradient error in the oceanic circulation. Implementation and testing of several vertical turbulence parameterizations (such as GOTM). Study for the pertinence and implementation of a wave-induced vertical mixing scheme. Fiddling with the bathymetry from ETOPO, fiddling with the tide components, fiddling with the boundary conditions. Do it and make it work! 1. Developping the hydrodynamical 3D model Guillaume

5. Development and/or Implementation of software code for format integration to import data delivered by large scale models. Testing nesting techniques between the large scale and local hydrodynamic properties (ex: adaptive open boundaries, flow relaxation scheme, radiation schemes, free surface elevation). Identification of sources for imposing the physical properties in the open boundary (ex: MW, Gulf of Cádiz, Azores Current, Wind). 2. Downscaling physics from large scale models to regional models Guillaume

6. Test the Water-Air interface module of the model Use of established methodologies for imposing atmospheric fluxes in the coastal model Couple the model to an ocean-atmosphere modelling system fed by high-resolution observations 3. Develop the Ocean-Atmosphere coupling with high resolution atmospheric forcing Guillaume

7. Adjust model parameters and coefficients to improve agreement between observed data and the model’s results. What is the best methodology to validate the model from satellite-data? How reliable this is? 4. Use of experimental data to validate the model’s results Guillaume

8. Work done so far Guillaume • Mohid Hydrodynamic’s technical manual: • Physics (Hydrodynamic’s equations, Tracer equations, Turbulent mixing) • Numerical Discretization • Test cases (1D vertical model, Freshwater cylinder, Rossby solitary wave, Isolated Seamount ...) • Application cases: Ria de Vigo, Tagus operational model, circulation off the portuguese coast, internal waves in submarine canyons (Nazaré)

9. Objective: Improve the results of existing prediction system for Tagus Estuary; Methodology: State of the art: Operational models, Data assimilation; Study of existing system and new approaches (boundaries, advection, turbulence): validation and error quantification; Implementation of data assimilation module: Kalman Filter techniques, Optimal Interpolation; Cost-benefit assessment of improvement options facing operational framework. P.h.D work: Operational Modelling in estuariesOutline Ângela

10. level space time P.h.D work: Operational Modelling in estuariesWork developed so far Ângela Data Assimilation 1D linear water level model: Kalman Filter 1D hydrodynamic model (shallow water equations): EnKF New configurations Bathymetry: Level 2 (Tagus Estuary) Boundary conditions: Level 1 (Portuguese Coast) Level 0 (North Atlantic) Tagus Estuary Prediction System Validation Water level measurements (tide gauges) - 1972 Future... measurements for model validation and data assimilation

11. Bidirectional coupling of the mesoscale atmospherical model MM5 and of the oceanographical model MOHID. Identification of the oceanic-atmosphere interaction’s effects: Compare with the results of the independent models Quantifiy the required resolution degree Influence of the coupling in the wind waves and in the oceanic mixed layer. Improve the Upwelling forecasts in the portuguese coast. Improve rainfall forecasts. Thesis objectives Rosa

12. An operational system of meteorological forecasting for the Iberian peninsula, with MM5: http://meteo.ist.utl.pt MM5 Operacional Rosa x = 81 km x = 27 km x = 9 km

13. Wind waves Changes in the superficial oceanic layer Runs operationally with ARPS model’s data (MeteoGalicia) http://maretec.mohid.com/ww3 WaveWatch III Rosa

14. Mass, momentum and energy flux Modelos Envolvidos Rosa Future: Now: Global Forecast System Atmosphere : MM5 ARPS Applied shear stress Radiation; Heat and mass flux Superficial rugosity Heat and mass flux; SST WaveWatchIII Shear Stress and superficial rugosity Surface currents Ocean:

15. Climate Change, Fisheries Pressure and Upwelling Ecosystems Ricardo Lemos Objectives: • Identify the causes for the evolution of the Portuguese coastal upwelling system in the 20th century • Assess the responses of primary productivity (seasonality, annual production, spatial distribution, etc) to the observed changes in upwelling • Estimate the impacts of changes in primary productivity on the food web • Estimate, in a virtual, stable ocean, the impacts of fisheries on the structure of Portuguese coastal ecosystems • Combine the two impact pathways into a single model Top down Bottom up Climate Change

16. Climate Change, Fisheries Pressure and Upwelling Ecosystems Ricardo Lemos The evolution of the upwelling regime off west Portugal, 1941-2000 Porto Lisbon Cape Carvoeiro Evolution of annual mean ocean temperature (ºC) in region C2, at different standard depths. Linear trend estimates (x0.01ºC/yr) are displayed on the right. Evolution of the April-September v-wind component at Porto, Cape Carvoeiro and Lisbon (9 yr running-means and long-term trend)

17. Work underway Ricardo Lemos Bayesian Dynamic Linear Models Spatial Process Noise Data = + yi = ei S(xi, b2) + Time 2 Time 3 Time 1 S(., b1) S(., b3) S(., b2)