Nemo-Nordic 1.0: A NEMO based ocean model for Baltic & North Seas, research and operational applications

Abstract

Abstract. We present Nemo-Nordic, a Baltic & North Sea model based on the NEMO ocean engine. Surrounded by highly industrialised countries, the Baltic and North seas, and their assets associated with shipping, fishing and tourism; are vulnerable to anthropogenic pressure and climate change. Ocean models providing reliable forecasts, and enabling climatic studies, are important tools for the shipping infrastructure and to get a better understanding of effects of climate change on the marine ecosystems. Nemo-Nordic is intended to come as a tool for both short term and long term simulations, and to be used for ocean forecasting as well as process and climatic studies. Here, the scientific and technical choices within Nemo-Nordic are introduced, and the reasons behind the design of the model and its domain, and the inclusions of the two seas, are explained. The model's ability to represent barotropic and baroclinic dynamics, as well as the vertical structure of the water column, is presented. Biases are shown and discussed. The short term capabilities of the model are presented, and especially its capabilities to represent sea level on an hourly timescale with a high degree of accuracy. We also show that the model can represent longer time scale, with a focus on the Major Baltic Inflows and the variability of deep water salinity in the Baltic Sea.

A regional atmosphere-ocean climate system model (CCLMv5.0clm7-NEMOv3.3-NEMOv3.6) over Europe including three marginal seas: on its stability and performance

“…Another NEMO set-up has been adapted to reproduce the barotropic and baroclinic dynamics, as well as the thermohaline structure, of the Baltic and North Sea basins. This is the so-called NEMO-NORDIC (Hordoir et al, 2018), whose ocean component is coupled to the sea ice model LIM3 (Vancoppenolle et al, 2009). In this study we use NEMO-NORDIC, based on NEMO version 3.3, including 25 the LIM3 sea ice model, with a resolution 2' (~ 0.03°~ 3km, 523x619 = 323737 grid points), 56 vertical levels and a numerical time step equal to 180 s. The initial conditions for three-dimensional potential temperature and salinity are provided by Janssen et al (1999) and the lateral boundary conditions in the North Sea are prescribed from ORAS4 reanalysis data (Balmaseda et al, 2013).…”

“…They showed that the coupled system was mostly able to simulate Mistral and Tramontane events with smaller biases than ERA-Interim. Akhtar et al (2017) used that system to show the impact of the horizontal grid resolution and the dynamic ocean coupling of the NEMO-MED in simulations with the COSMO-CLM 25 during the period from 1979 to 2009 andHordoir et al (2018) coupled the NEMO-NORDIC model to CCLM and showed that the high biases presented when compared to observations were of the same magnitude as other COSMO-CLM studies and smaller than for the uncoupled version. Their study covered the period 1979-2007 and they argued that the quality of the model was a result of using ERA-Interim as driving data, whose quality is already high.…”

Combining seascape connectivity with cumulative impact assessment in support of ecosystem‐based marine spatial planning

“…Climatological data from a number of different databases for the Baltic Sea and the North Sea provided freshwater runoff. Validation of the NEMO‐Nordic model has shown that the model is able to correctly represent the SSH, both tidally induced and wind driven (Hordoir et al., 2019).…”

Baltic Sea Operational Oceanography—A Stimulant for Regional Earth System Research

“…Ocean, ice and oil drift forecast models BSH-Cmod, BSH-Dmod, HIROMB, and HELMI had been developed and operationalized in the early and mid-1990s (Haapala and Leppäranta, 1996;Dick et al, 2001;Wilhelmsson, 2002;Funkquist and Kleine, 2007). They are currently updated by more advanced coupled oceanice forecasting systems HBM (HIROMB-BOOS Model, Berg and Poulsen, 2012), NEMO-Nordic (Hordoir et al, 2018), and GETM (General Estuarine Transport Model, Burchard and Bolding, 2002;Büchmann and Söderkvist, 2016). HBM is a dynamically two-way nested model with excellent hybrid parallel computing performance (Poulsen et al, 2014).…”