Development of the MOHID consortium Presentation of 4 PhD thesis works

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) Thesis menu

Guillaume  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. Thesis main goals

Guillaume 1. Developping the hydrodynamical 3D model 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!

Guillaume 2. Downscaling physics from large scale models to regional models 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).

Guillaume 3. Develop the Ocean-Atmosphere coupling with high resolution atmospheric forcing 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

Guillaume 4. Use of experimental data to validate the model’s results 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?

Guillaume Work done so far 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)

Ângela P.h.D work: Operational Modelling in estuaries Outline 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.

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

Rosa Thesis objectives 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.

Rosa MM5 Operacional An operational system of meteorological forecasting for the Iberian peninsula, with MM5:  x = 81 km  x = 27 km  x = 9 km

Rosa WaveWatch III Wind waves Changes in the superficial oceanic layer Runs operationally with ARPS model’s data (MeteoGalicia)

Rosa Modelos Envolvidos Global Forecast System WaveWatchIII Ocean: Now:Future: Atmosphere : MM5ARPS Mass, momentum and energy flux Heat and mass flux; SST Superficial rugosity Surface currents Applied shear stress Shear Stress and superficial rugosity Radiation; Heat and mass flux

Ricardo Lemos Climate Change, Fisheries Pressure and Upwelling Ecosystems 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 Climate Change Top down Bottom up

Ricardo Lemos 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. The evolution of the upwelling regime off west Portugal, Climate Change, Fisheries Pressure and Upwelling Ecosystems Porto Cape Carvoeiro Lisbon Evolution of the April-September v-wind component at Porto, Cape Carvoeiro and Lisbon (9 yr running-means and long-term trend)

Ricardo Lemos Work underway Time 1 Time 2Time 3 Data Spatial ProcessNoise S(.,  1 ) S(.,  2 ) S(.,  3 ) =+ yiyi S(x i,  2 ) ii = + Bayesian Dynamic Linear Models