What is NEMO?

NEMO (Nucleus for European Modelling of the Ocean, www.nemo-ocean.eu/) is a state-of-the-art modelling framework widely used in ocean and climate research, as well as in operational forecasting. It provides a flexible and modular system for simulating the ocean in its physical, sea-ice, and biogeochemical components.
The NEMO model (Madec .et al., 2024, 2019) continuously evolves through the improvement of existing engines and the development of new modelling capabilities, the integration of additional components and interfaces with other models, and continuous adaptation to emerging high-performance computing architectures. The scientific and technical development is overseen by the NEMO European Consortium, which includes leading European institutions such as CMCC (Italy), CNRS (France), MOI (France), NERC-NOC (UK) and Met Office (UK). The NEMO Developers Committee (NDC), co-led by CMCC, defines the long-term development strategy and annual work plans. The NEMO System Team is responsible for integrating new scientific developments, maintaining the NEMO code, and supporting the user community through documentation, training, and regular releases.

The NEMO framework consists of three main components:

• NEMO-OCE is the core ocean component of the NEMO model. It solves the primitive equations for ocean dynamics and thermodynamics, using a flexible grid system and multiple vertical coordinate options;
• NEMO-ICE (SI3: Sea-Ice Integrated Initiative; Vancoppenolle et al., 2023) is the sea ice module that resolves ice dynamics and thermodynamics, including brine inclusions and subgrid-scale variations in ice thickness;
• NEMO-TOP (Tracers in Ocean Paradigm; NEMO TOP Working Group, 2018) is responsible for simulating on/offline oceanic tracers transport and biogeochemical processes.

NEMO also integrates several specialised software packages to enhance its capabilities, such as AGRIF for two-way nesting and regional refinement, XIOS for efficient input/output management, and various utilities for data pre-processing, configuration setup, and post-simulation analysis. NEMO can also be coupled with other Earth system components through OASIS.

NEMO is open-source and freely available, including the source code, tools, reference configurations, and a range of test cases for training and functionality evaluation. Since 2022, the source code has been hosted, developed, and maintained on the official NEMO GitLab Repository, supported by the Discourse forum, which is actively maintained as the main platform for technical support, community discussion, and knowledge sharing among users.

How does CMCC contribute to and use NEMO?

A group of CMCC researchers participates in the NEMO System Team, actively contributing to developments in High Performance Computing (HPC; Calvert et al., 2022; Epicoco et al. 2020 & 2021; Mele et al.,2022), Sea Ice, Tracer in the Ocean Paradigm (TOP) and Air-Sea interaction through dedicated working groups. They are from:

• ADIC: Italo Epicoco, Francesca Mele
• ESYDA division: Dorotea Iovino, Tomas Lovato, Francesco Cocetta
• ROFS division: Diana Martins, Aimie Moulin

CMCC researchers who directly participate in the Developers Committee are Aimie Moulin (CMCC NEMO Officer), Dorotea Iovino (co-chair of the NDC), Italo Epicoco (leader of HPC working group), Tomas Lovato (leader of TOP working group), and Momme Butenschon (CMCC NEMO Expert). Dr. Simona Masina is the CMCC Representative at the NEMO Steering Committee.

NEMO is the core of the operational ocean modelling systems developed at CMCC, covering configurations from the global to the regional and sub-regional scale.
The Global Ocean Forecasting System (GOFS16; Masina et al., 2020) and the CMCC Global Ocean Reanalyses System (CGLORS) are based on global ocean-ice NEMO configurations at eddy-rich (Iovino et al., 2016) and eddy-permitting horizontal resolutions coupled with the variational data assimilation system OceanVar (Cipollone et al., 2020). GOFS16 has provided daily short-term forecasts and nowcasts of ocean and sea ice conditions since 2017, whereas CGLORS reconstructs the state of the global ocean over recent decades. CGLORS (Storto et al., 2019) is available in versions, v5 (starting in 1980; Storto et al., 2016) and v7 (starting in 1993), and is extended twice a year.
At the regional scale, the Mediterranean Forecasting System (MedFS, 1/24°) and the Black Sea Forecasting System (BSFS, 1/40°) both rely on NEMO-based coupled hydrodynamic–wave configurations, to deliver medium-term forecasts (Clementi et al., 2017; Coppini et al., 2023; Ciliberti et al., 2022), and reanalyses (Escudier et al., 2021; Lima et al., 2021) in the framework of the Copernicus Marine Service.
At a sub-regional scale, a NEMO based configuration with about 2km horizontal resolution covering the Adriatic basin and the Northern Ionian Sea is one-way coupled with the atmospheric model WRF, the hydrological model WRFHydro, the marine wave model WWIII and biogeochemistry model BFM to represent the coastal water cycle and enable coastal climate downscaling over centennial time scales (Verri et al., 2024). A two-way nesting approach, based on the AGRIF tool, further refines the resolution to ~500 m in the Northern Adriatic, reaching eddy-permitting scales and accounting for cross-scale feedback

At the global scale, the CMCC Operational Seasonal Prediction System (SPS) employs an eddy-permitting NEMO configuration integrated into the CMCC Climate Model to deliver six-month ensemble forecasts. The NEMO ocean model is widely used in climate studies and CMIP-related experiments conducted at CMCC. For instance, the CMCC Earth System Model (CMCC-ESM2; Lovato et al., 2022) and CMCC Climate Model (CMCC-CM2; Cherchi et al., 2019), both of which participated in the CMIP6 intercomparison project, included NEMO version 3.6 as the ocean component (at the nominal 1° and 1/4° horizontal resolutions). CMCC also contributed to the Ocean Model Intercomparison Project (OMIP) using global ocean configurations based on NEMO v3.6, employing a hierarchy of global resolutions up to 1/16° resolution (Iovino et al., 2023).

NEMO is also one of the ocean models integrated into the Structured and Unstructured grid Relocatable Ocean Platform for Forecasting (SURF) (Trotta et al., 2016; Trotta et al., 2021; surf-platform.org) SURF is an open-source platform designed to set-up, execute and analyse high-resolution nested ocean models in any region within a large-scale Ocean Forecasting, Analysis and Reanalysis System.  The platform is distributed as a virtual machine and as Docker container images, ensuring portable deployment across a wide range of computational environments, including cloud infrastructures, desktop computers, and laptops. SURF is a valuable tool to support Decision Support System (DSS) by providing high-resolution ocean forecasts crucial for applications like oil spill monitoring, search and rescue operations, navigation routing, fisheries management, and coastal tourism.

References

Calvert, D., Bell, M., Mele, F., Epicoco, I., Masson, S., Glover, M., & Madec, G. (2022, May). Loop blocking (“tiling”) in NEMO. EGU General Assembly 2022 (EGU22-13438).

Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., et al. (2019). Global mean climate and main patterns of variability in the CMCC-CM2 coupled model. Journal of Advances in Modeling Earth Systems, 11(1), 185–209. https://doi.org/10.1029/2018MS001369

Ciliberti, S. A., Jansen, E., Coppini, G., Peneva, E., Azevedo, D., Causio, S., et al. (2022). The Black Sea Physics Analysis and Forecasting System within the framework of the Copernicus Marine Service. Journal of Marine Science and Engineering, 10(1), 48. https://doi.org/10.3390/jmse10010048

Cipollone, A., Storto, A., & Masina, S. (2020). Implementing a parallel version of a variational scheme in a global assimilation system at eddy-resolving resolution. Journal of Atmospheric and Oceanic Technology, 37(10), 1865–1876. https://doi.org/10.1175/JTECH-D-20-0023.1

Clementi, E., Oddo, P., Drudi, M., Pinardi, N., Korres, G., & Grandi, A. (2017). Coupling hydrodynamic and wave models: First step and sensitivity experiments in the Mediterranean Sea. Ocean Dynamics, 67, 1293–1312. https://doi.org/10.1007/s10236-017-1087-7,

Coppini, G., Clementi, E., Cossarini, G., Salon, S., Korres, G., Ravdas, M., et al. (2023). The Mediterranean Forecasting System – Part 1: Evolution and performance. Ocean Science, 19, 1483–1516.  https://doi.org/10.5194/os-19-1483-2023

Epicoco, I., Mele, F., Mocavero, S., Chiarelli, M., D’Anca, A., & Aloisio, G. (2020, May). Refactoring the memory access pattern to improve computational performance in NEMO. EGU General Assembly 2020 (EGU2020-9723).

Epicoco, I., Mocavero, S., Mele, F., D’Anca, A., & Aloisio, G. (2021, April). New communication strategies in NEMO. EGU General Assembly 2021 (EGU21-4762).

Escudier, R., Clementi, E., Cipollone, A., Pistoia, J., Drudi, M., Grandi, A., et al. (2021). A high-resolution reanalysis for the Mediterranean Sea. Frontiers in Earth Science, 9, 702285. https://doi.org/10.3389/feart.2021.702285

Iovino, D., Fogli, P. G., & Masina, S. (2023). Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2). Geoscientific Model Development, 16, 6127–6159. https://doi.org/10.5194/gmd-16-6127-2023

Iovino, D., Masina, S., & Fogli, P. G. (2018, December). Resolving mesoscale dynamics in global ocean. AGU Fall Meeting 2018, Washington, DC, United States.

Iovino, D., Masina, S., Storto, A., Cipollone, A., & Stepanov, V. N. (2016). A 1/16° eddying simulation of the global NEMO sea-ice–ocean system. Geoscientific Model Development, 9, 2665–2684. https://doi.org/10.5194/gmd-9-2665-2016

Lima, L., Ciliberti, S. A., Aydoğdu, A., Masina, S., Escudier, R., Cipollone, A., et al. (2021). Climate signals in the Black Sea from a multidecadal eddy-resolving reanalysis. Frontiers in Marine Science, 8, 710973. https://doi.org/10.3389/fmars.2021.710973

Lovato, T., Peano, D., Butenschön, M., Materia, S., Iovino, D., Scoccimarro, E., et al. (2022). CMIP6 simulations with the CMCC Earth System Model (CMCC-ESM2). Journal of Advances in Modeling Earth Systems. https://doi.org/10.1029/2021MS002814

Madec, G., & NEMO System Team. (2019). NEMO ocean engine (Scientific Notes of Climate Modelling Center, 27). Institut Pierre-Simon Laplace (IPSL). https://doi.org/10.5281/zenodo.6334656

Madec, G., & NEMO System Team. (2024). NEMO ocean engine reference manual. Zenodo. https://doi.org/10.5281/zenodo.1464816

Masina, S., Cipollone, A., Iovino, D., Ciliberti, S., Coppini, G., Lecci, R., et al. (2020, February). GOFS16: A global ocean-sea ice forecast system at eddying resolution. Ocean Sciences Meeting 2020, San Diego, CA, United States.

Mele, F., Epicoco, I., Mocavero, S., Calvert, D., & Bell, M. (2022, May). Preparing NEMO 4.2: The new NEMO modeling framework for the next generation HPC infrastructures. EGU General Assembly 2022 (EGU22-13484).

NEMO Sea Ice Working Group. (2019). Sea ice modelling integrated initiative (SI3): The NEMO sea ice engine (Scientific Notes of Climate Modelling Center, 31). Institut Pierre-Simon Laplace (IPSL).

NEMO TOP Working Group. (2018). Tracer in Ocean Paradigm (TOP) — The NEMO passive tracer engine, Scientific Notes of Climate Modelling Center (28) – ISSN 1288-1619, Institut Pierre-Simon Laplace (IPSL)

Storto, A., & Masina, S. (2016). C-GLORSv5: An improved multipurpose global ocean eddy-permitting physical reanalysis. Earth System Science Data, 8(2), 679–696. https://doi.org/10.5194/essd-8-679-2016

Storto, A., Masina, S., Simoncelli, S., Iovino, D., Cipollone, A., Drevillon, M., et al. (2019). The added value of the multi-system spread information for ocean heat content and steric sea level investigations in the CMEMS GREP ensemble reanalysis product. Climate Dynamics, 53, 287–312. https://doi.org/10.1007/s00382-018-4585-5

Trotta, F., Fenu, E., Pinardi, N., Bruciaferri, D., Giacomelli, L., Federico, I., & Coppini, G. (2016). A structured and unstructured grid relocatable ocean platform for forecasting (SURF). Deep Sea Research Part II: Topical Studies in Oceanography, 133, 54–75. https://doi.org/10.1016/j.dsr2.2016.05.004

Trotta, F., Federico, I., Pinardi, N., Coppini, G., Causio, S., Jansen, E., Iovino, D., & Masina, S. (2021). A relocatable ocean modeling platform for downscaling to shelf–coastal areas to support disaster risk reduction. Frontiers in Marine Science, 8, 642815. https://doi.org/10.3389/fmars.2021.642815

Verri, G, L. Furnari, M. Gunduz, A. Senatore, V. Santos da Costa, A. De Lorenzis, G. Fedele, I. Manco, L. Mentaschi, E. Clementi, G. Coppini, P. Mercogliano, G. Mendicino, N. Pinardi, (2024). Climate projections of the Adriatic Sea: the role of river release, Frontiers in Climate