Uncertainty estimation for operational ocean forecast products-a multi-model ensemble for the North Sea and the Baltic Sea

Multi-model ensembles for sea surface temperature (SST), sea surface salinity (SSS), sea surface currents (SSC), and water transports have been developed for the North Sea and the Baltic Sea using outputs from several operational ocean forecasting models provided by different institutes. The individual models differ in model code, resolution, boundary conditions, atmospheric forcing, and data assimilation. The ensembles are produced on a daily basis. Daily statistics are calculated for each parameter giving information about the spread of the forecasts with standard deviation, ensemble mean and median, and coefficient of variation. High forecast uncertainty, i.e., for SSS and SSC, was found in the Skagerrak, Kattegat (Transition Area between North Sea and Baltic Sea), and the Norwegian Channel. Based on the data collected, longer-term statistical analyses have been done, such as a comparison with satellite data for SST and evaluation of the deviation between forecasts in temporal and spatial scale. Regions of high forecast uncertainty for SSS and SSC have been detected in the Transition Area and the Norwegian Channel where a large spread between the models might evolve due to differences in simulating the frontal structures and their movements. A distinct seasonal pattern could be distinguished for SST with high uncertainty between the forecasts during summer. Forecasts with relatively high deviation from the multi-model ensemble (MME) products or the other individual forecasts were detected for each region and each parameter. The comparison with satellite data showed that the error of the MME products is lowest compared to those of the ensemble members

Copernicus Marine Service Ocean State Report

This is the final version. Available from Taylor & Francis via the DOI in this record

Recent progress in performance evaluations and near real-time assessment of operational ocean products

Operational ocean forecast systems provide routine marine products to an ever-widening community of users and stakeholders. The majority of users need information about the quality and reliability of the products to exploit them fully. Hence, forecast centres have been developing improved methods for evaluating and communicating the quality of their products. Global Ocean Data Assimilation Experiment (GODAE) OceanView, along with the Copernicus European Marine Core Service and other national and international programmes, has facilitated the development of coordinated validation activities among these centres. New metrics, assessing a wider range of ocean parameters, have been defined and implemented in real-time. An overview of recent progress and emerging international standards is presented here. © 2015 Institut de Recherche pour le Développement (IRD)

Recent progress in performance evaluations and near real-time assessment of operational ocean products

Operational ocean forecast systems provide routine marine products to an ever-widening community of users and stakeholders. The majority of users need information about the quality and reliability of the products to exploit them fully. Hence, forecast centres have been developing improved methods for evaluating and communicating the quality of their products. Global Ocean Data Assimilation Experiment (GODAE) OceanView, along with the Copernicus European Marine Core Service and other national and international programmes, has facilitated the development of coordinated validation activities among these centres. New metrics, assessing a wider range of ocean parameters, have been defined and implemented in real-time. An overview of recent progress and emerging international standards is presented here

Copernicus Marine Service Ocean State Report, Issue 5

Demersal species play a fundamental role in fisheries, thus understanding their distribution and abundance through bottom trawl surveys is crucial for stock and fisheries management. Oceanographic (e.g. biogeochemical, physical) and fishing covariates might be considered, in addition to spatio-temporal variables (latitute, longitude, depth, year and month), to better explain trawl survey data. Here, we analyse biomass indices (kg/km2) for European hake, common sole, mantis shrimp, red mullet and common cuttlefish from scientific trawl surveys carried out in the Adriatic Sea and the Western Ionian Sea. We used three different Generalised Additive Model (GAM) approaches (Gaussian, Tweedie and Delta) to fit and predict species biomass distribution. In order to evaluate trade-offs in using different covariates, we compared the results obtained from GAM approaches based only on spatiotemporal variables and GAMs including also oceanographic and fishing effort covariates. The Delta-GAM approach performed better for European hake, mantis shrimp and common cuttlefish, while GAMs based on Gaussian and Tweedie were performing better for the red mullet and common sole, respectively. The results highlighted that adding specific oceanographic and effort covariates to spatiotemporal variables improved the performances of spatial distribution models especially for European hake, mantis shrimp and red mullet. Significant additional explanatory variables were bottom temperature, bottom dissolved oxygen, salinity, particulate organic carbon, and fishing effort for European hake; the same variables and pH for mantis shrimp; chlorophyll-a, pH, sea surface temperature, bottom dissolved oxygen, nitrate and effort for the red mullet; phosphate and salinity for common sole; bottom temperature, bottom dissolved oxygen, and phosphate for the common cuttlefish. The findings highlight that more accurate estimates of spatial distribution of demersal species biomass from trawl survey data can generally be obtained by integrating oceanographic variables and effort in GAMs approaches with potential impacts on stock assessment and essential fish habitats identification