Tintoré et al. Sustained Mediterranean Observing Forecasting SystemThe Mediterranean community represented in this paper is the result of more than 30 years of EU and nationally funded coordination, which has led to key contributions in science concepts and operational initiatives. Together with the establishment of operational services, the community has coordinated with universities, research centers, research infrastructures and private companies to implement advanced multi-platform and integrated observing and forecasting systems that facilitate the advancement of operational services, scientific achievements and mission-oriented innovation. Thus, the community can respond to societal challenges and stakeholders needs, developing a variety of fit-for-purpose services such as the Copernicus Marine Service. The combination of state-of-the-art observations and forecasting provides new opportunities for downstream services in response to the needs of the heavily populated Mediterranean coastal areas and to climate change. The challenge over the next decade is to sustain ocean observations within the research community, to monitor the variability at small scales, e.g., the mesoscale/submesoscale, to resolve the sub-basin/seasonal and inter-annual variability in the circulation, and thus establish the decadal variability, understand and correct the model-associated biases and to enhance model-data integration and ensemble forecasting for uncertainty estimation. Better knowledge and understanding of the level of Mediterranean variability will enable a subsequent evaluation of the impacts and mitigation of the effect of human activities and climate change on the biodiversity and the ecosystem, which will support environmental assessments and decisions. Further challenges include extending the science-based added-value products into societal relevant downstream services and engaging with communities to build initiatives that will contribute to the 2030 Agenda and more specifically to SDG14 and the UN's Decade of Ocean Science for sustainable development, by this contributing to bridge the science-policy gap. The Mediterranean observing and forecasting capacity was built on the basis of community best practices in monitoring and modeling, and can serve as a basis for the development of an integrated global ocean observing system.
www.uliege.be Croatia Ruder Boskovic Institute (IRB) www.irb.hr/eng Cyprus Joint Research and Development Center-ORION www.orioncyprus.org Denmark Joint GeoMETOC Support Center www.mgeometoc-coe.org " Danish Meteorological Institute (DMI) http://ocean.dmi.dk/english/index.php Finland Finnish Meteorological Institute (FMI) en.ilmatieteenlaitos.fi France SHOM www.shom.fr " Meteo-France www.meteofrance.com Germany Federal Maritime and Hydrographic Agency (BSH) www.bsh.de " Helmholtz-Zentrum Geesthacht (HZG) www.hzg.de Greece Democritus University of Thrace (DUTH) www.duth.gr " Foundation for Research and Technology-Hellas (FORTH) http://poseidon.hcmr.gr/ " Hellenic Centre for Marine Research (HCMR) www.hcmr.gr Ireland Marine Institute (MI) www.marine.ie Israel Israel Oceanographic and Limnological Research (IOLR) www.ocean.org.il/mainpageeng.asp Italy ARPAe www.arpae.it " ARPA FVG www.arpa.fvg.it " ARPAL www.arpal.gov.it " EuroMediterranean Center on Climate Change (CMCC) www.cmcc.it " Consorzio LaMMa http://www.lamma.rete.toscana.it/en/currents-lamma-roms-model " ENEA www.enea.it " Istituto Nazionale di Geofisica e Vulcanologia (INGV) medforecast.bo.ingv.it " National Institute of Oceanography and Applied Geophysics (OGS) www.inogs.it " Physics Dept. Bologna Univ. (Unibo) www.physics-astronomy.unibo.it/en " CNR-IAS www.seaforecast.cnr.it " CNR-ISMAR www.ismar.cnr.it Latvia University of Latvia (UL) www.lu.lv/en Malta University of Malta (UM) www.um.edu.mt/science/geosciences/physicaloceanography Netherlands Deltares www.deltares.nl/en " Koninklijk Nederlands Meteorologisch Instituut (KNMI) www.knmi.nl/home Norway Nansen Environmental and Remote Sensing Center (NERSC) www.nersc.no
Available climate change projections, which can be used for quantifying future changes in marine and coastal ecosystems, usually consist of a few scenarios. Studies addressing ecological impacts of climate change often make use of a low- (RCP2.6), moderate- (RCP4.5) or high climate scenario (RCP8.5), without taking into account further uncertainties in these scenarios. In this research a methodology is proposed to generate further synthetic scenarios, based on existing datasets, for a better representation of climate change induced uncertainties. The methodology builds on Regional Climate Model scenarios provided by the EURO-CORDEX experiment. In order to generate new realizations of climate variables, such as radiation or temperature, a hierarchical Bayesian model is developed. In addition, a parameterized time series model is introduced, which includes a linear trend component, a seasonal shape with varying amplitude and time shift, and an additive residual term. The seasonal shape is derived with the non-parametric locally weighted scatterplot smoothing, and the residual term includes the smoothed variance of residuals and independent and identically distributed noise. The distributions of the time series model parameters are estimated through Bayesian parameter inference with Markov chain Monte Carlo sampling (Gibbs sampler). By sampling from the predictive distribution numerous new statistically representative synthetic scenarios can be generated including uncertainty estimates. As a demonstration case, utilizing these generated synthetic scenarios and a physically based ecological model (Delft3D-WAQ) that relates climate variables to ecosystem variables, a probabilistic simulation is conducted to further propagate the climate change induced uncertainties to marine and coastal ecosystem indicators.
Prediction systems, such as the coastal ecosystem models, often incorporate complex non-linear ecological processes. There is an increasing interest in the use of probabilistic forecasts instead of deterministic forecasts in cases where the inherent uncertainties in the prediction system are important. The primary goal of this study is to set up an operational ensemble forecasting system for the prediction of the Chlorophyll-a concentration in coastal waters, using the Generic Ecological Model. The input ensemble is generated from perturbed model process parameters and external forcings through Latin Hypercube Sampling with Dependence. The forecast performance of the ensemble prediction is assessed using several forecast verification metrics that can describe the forecast accuracy, reliability and discrimination. The verification is performed against in-situ measurements and remote sensing data. The ensemble forecast moderately outperforms the deterministic prediction at the coastal in-situ measurement stations. The proposed ensemble forecasting system is therefore a promising tool to provide enhanced water quality prediction for coastal ecosystems which, with further inclusion of other uncertainty sources, could be used for operational forecasting.
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