Modelling primary production: multitude of theories, or multitude of languages? - Jozef Skákala; Shubha Sathyendranath; Yuri Artioli; Deep S. Banerjee; Heather Bouman; Robert J. W. Brewin; Momme Butenschön; Stefano Ciavatta; Stephanie Dutkiewicz; Yanna Fidai; David Ford; Grinson George; et al. (Ocean Science, 2026)
Marine primary production, converting approximately 50 Gt of inorganic carbon into organic carbon per year, is an important component of the global carbon cycle, and a major determinant of past, present and future climate. Large-scale, long-term estimates of marine primary production rely primarily on two types of models: satellite-based models that make extensive use of remote-sensing data, and ecosystem models providing numerical simulation of ecological processes embedded in general ocean circulation models. Intercomparison exercises of model outputs (both within and across the two model types) have consistently revealed high discrepancies between estimated global ocean primary production, including divergent magnitudes and even opposite trends. Model-observation comparisons are also complex, because paucity of data, differences in measurement techniques, and evolving methodologies could all lead to difficulties with the interpretation of results. These uncertainties limit the applications of primary production models (both satellite-based and ecosystem), especially in the climate context, where an important question is whether climate change will drive significant future changes in regional or global primary production. Both satellite-based and ecosystem models rely on a range of fixed model parameters, whose values need to be carefully estimated and tested. In this paper, we suggest that such model parameters represent an underappreciated but important source of inter-model differences. With the proliferation of both satellite and in situ observations of relevant variables at global scales, and the availability of powerful statistical tools such as data assimilation and machine learning, we argue that time is right to systematically examine model parameters, gaining both better insights into parameter values and how those values might vary in space and time. We argue that such spatio-temporal parameter variability can be theoretically justified for ecosystem models with complexity similar to those commonly used within Earth System Models (ESMs) in climate studies. The spatially and temporally varying parameter values could serve to unify models that are structurally different. An important aspect of this unification could be the ability to infer the spatio-temporal variability of parameters in the less complex models from the emergent behaviour of the more complex ones. This could include ecosystem model simulations of nutrients, temperature, phytoplankton classes, or vertical distributions informing satellite-based models. We conclude that better understanding of model parameter roles and integration (or inter-calibration) of different types of models could reduce discrepancies among the primary production models and improve the reliability of marine primary production projections.

Effects of sea ice on Baltic Sea eutrophication - Ove Parn; Olaf Duteil; Elisa Garcia-Gorriz; Nuno Ferreira-Cordeiro; Gennadi Lessin; Diego Macias; Svetla Miladinova; Chiara Piroddi; Luca Polimene; Natalia Serpetti; Adolf Stips (Marine Pollution Bulletin, 2024)

Future digital twins: emulating a highly complex marine biogeochemical model with machine learning to predict hypoxia - Jozef Skákala; Katie Awty-Carroll; Prathyush P. Menon; Ke Wang; Gennadi Lessin (Frontiers in Marine Science, 2023)
The Machine learning (ML) revolution is becoming established in oceanographic research, but its applications to emulate marine biogeochemical models are still rare. We pioneer a novel application of machine learning to emulate a highly complex physical-biogeochemical model to predict marine oxygen in the shelf-sea environment. The emulators are developed with intention of supporting future digital twins for two key stakeholder applications: (i) prediction of hypoxia for aquaculture and fisheries, (ii) extrapolation of oxygen from marine observations. We identify the key drivers behind oxygen concentrations and determine the constrains on observational data for a skilled prediction of marine oxygen across the whole water column. Through this we demonstrate that ML models can be very useful in informing observation measurement arrays. We compare the performance of multiple different ML models, discuss the benefits of the used approaches and identify outstanding issues, such as limitations imposed by the spatio-temporal resolution of the training/validation data.

Nitrous oxide and methane in a changing Arctic Ocean - Andrew P. Rees; Hermann W. Bange; Damian L. Arévalo-Martínez; Yuri Artioli; Dawn M. Ashby; Ian Brown; Hanna I. Campen; Darren R. Clark; Vassilis Kitidis; Gennadi Lessin; Glen A. Tarran; Carol Turley (Ambio, 2022)
Human activities are changing the Arctic environment at an unprecedented rate resulting in rapid warming, freshening, sea ice retreat and ocean acidification of the Arctic Ocean. Trace gases such as nitrous oxide (N 2 O) and methane (CH 4 ) play important roles in both the atmospheric reactivity and radiative budget of the Arctic and thus have a high potential to influence the region’s climate. However, little is known about how these rapid physical and chemical changes will impact the emissions of major climate-relevant trace gases from the Arctic Ocean. The combined consequences of these stressors present a complex combination of environmental changes which might impact on trace gas production and their subsequent release to the Arctic atmosphere. Here we present our current understanding of nitrous oxide and methane cycling in the Arctic Ocean and its relevance for regional and global atmosphere and climate and offer our thoughts on how this might change over coming decades.

The role of a changing Arctic Ocean and climate for the biogeochemical cycling of dimethyl sulphide and carbon monoxide - Hanna I. Campen; Damian L. Arévalo-Martínez; Yuri Artioli; Ian J. Brown; Vassilis Kitidis; Gennadi Lessin; Andrew P. Rees; Hermann W. Bange (Ambio, 2022)
Dimethyl sulphide (DMS) and carbon monoxide (CO) are climate-relevant trace gases that play key roles in the radiative budget of the Arctic atmosphere. Under global warming, Arctic sea ice retreats at an unprecedented rate, altering light penetration and biological communities, and potentially affect DMS and CO cycling in the Arctic Ocean. This could have socio-economic implications in and beyond the Arctic region. However, little is known about CO production pathways and emissions in this region and the future development of DMS and CO cycling. Here we summarize the current understanding and assess potential future changes of DMS and CO cycling in relation to changes in sea ice coverage, light penetration, bacterial and microalgal communities, pH and physical properties. We suggest that production of DMS and CO might increase with ice melting, increasing light availability and shifting phytoplankton community. Among others, policy measures should facilitate large-scale process studies, coordinated long term observations and modelling efforts to improve our current understanding of the cycling and emissions of DMS and CO in the Arctic Ocean and of global consequences.

Effects of Nutrient Management Scenarios on Marine Eutrophication Indicators: A Pan-European, Multi-Model Assessment in Support of the Marine Strategy Framework Directive - René Friedland; Diego Macias; Gianpiero Cossarini; Ute Daewel; Claude Estournel; Elisa Garcia-Gorriz; Bruna Grizzetti; Marilaure Grégoire; Bo Gustafson; Sofia Kalaroni; Onur Kerimoglu; Paolo Lazzari; et al. (Frontiers in Marine Science, 2021)
A novel pan-European marine model ensemble was established, covering nearly all seas under the regulation of the Marine Strategy Framework Directive (MSFD), with the aim of providing a consistent assessment of the potential impacts of riverine nutrient reduction scenarios on marine eutrophication indicators. For each sea region, up to five coupled biogeochemical models from institutes all over Europe were brought together for the first time. All model systems followed a harmonised scenario approach and ran two simulations, which varied only in the riverine nutrient inputs. The load reductions were evaluated with the catchment model GREEN and represented the impacts due to improved management of agriculture and wastewater treatment in all European river systems. The model ensemble, comprising 15 members, was used to assess changes to the core eutrophication indicators as defined within MSFD Descriptor 5. In nearly all marine regions, riverine load reductions led to reduced nutrient concentrations in the marine environment. However, regionally the nutrient input reductions led to an increase in the non-limiting nutrient in the water, especially in the case of phosphate concentrations in the Black Sea. Further core eutrophication indicators, such as chlorophyll-a, bottom oxygen and the Trophic Index TRIX, improved nearly everywhere, but the changes were less pronounced than for the inorganic nutrients. The model ensemble displayed strong consistency and robustness, as most if not all models indicated improvements in the same areas. There were substantial differences between the individual seas in the speed of response to the reduced nutrient loads. In the North Sea ensemble, a stable plateau was reached after only three years, while the simulation period of eight years was too short to obtain steady model results in the Baltic Sea. The ensemble exercise confirmed the importance of improved management of agriculture and wastewater treatments in the river catchments to reduce marine eutrophication. Several shortcomings were identified, the outcome of different approaches to compute the mean change was estimated and potential improvements are discussed to enhance policy support. Applying a model ensemble enabled us to obtain highly robust and consistent model results, substantially decreasing uncertainties in the scenario outcome.

Carbon on the Northwest European Shelf: Contemporary Budget and Future Influences - Oliver Legge; Martin Johnson; Natalie Hicks; Tim Jickells; Markus Diesing; John Aldridge; Julian Andrews; Yuri Artioli; Dorothee C. E. Bakker; Michael T. Burrows; Nealy Carr; Gemma Cripps; et al. (Frontiers in Marine Science, 2020)

Time Scales of Benthic Macrofaunal Response to Pelagic Production Differ Between Major Feeding Groups - Gennadi Lessin; Jorn Bruggeman; Caroline Louise McNeill; Stephen Widdicombe (Frontiers in Marine Science, 2019)

Modelling Marine Sediment Biogeochemistry: Current Knowledge Gaps, Challenges, and Some Methodological Advice for Advancement - Gennadi Lessin; Yuri Artioli; Elin Almroth-Rosell; Jerry C. Blackford; Andrew W. Dale; Ronnie N. Glud; Jack J. Middelburg; Roberto Pastres; Ana M. Queirós; Christophe Rabouille; Pierre Regnier; Karline Soetaert; et al. (Frontiers in Marine Science, 2018)

Identification of the coastal zone of the central and eastern Gulf of Finland by numerical modeling, measurements, and remote sensing of chlorophyll a - Gennadi Lessin; Viktoria Ossipova; Inga Lips; Urmas Raudsepp (Hydrobiologia, 2009)

Water quality assessment using integrated modeling and monitoring in Narva Bay, Gulf of Finland - Gennadi Lessin; Urmas Raudsepp (Environmental Modeling & Assessment, 2006)