Incorporating genomic data into marine ecosystem modeling represents a pivotal advancement in our ability to comprehend and predict the intricate dynamics of oceanic processes. Genomic technologies have provided unprecedented insights into the relationships between microbial communities and biochemical cycles within marine environments, including the Arctic Ocean. By deciphering the genetic makeup and metabolic potential of marine microbes, these technologies have revolutionized our understanding of marine ecology and biogeochemistry. Traditionally, marine ecosystem models have relied on empirical data and simplified representations of microbial metabolic processes. This limitation inhibits their ability to accurately capture the complexities of microbial communities and their interactions with biogeochemical cycles. The unprecedented availability of genomic data from ocean observations represents a groundbreaking advancement in marine science. This exponential growth presents both opportunities and challenges for marine researchers, since the integration of microbial genomic data into biogeochemical models also poses several challenges. One of the primary challenges is the translation of genetic information into model parameters that can be effectively incorporated into existing modeling frameworks. This requires a deeper understanding of the functional significance of specific genes and metabolic pathways in driving biogeochemical processes. In the CHANGE project, we propose to begin unlocking this challenge through interdisciplinary collaboration between highly experient marine biologists, genomic scientists, and modelers. This interdisciplinary project aims to bridge the gap between genomic data and biogeochemical modeling. Our goal is to overcome the challenges associated with integrating genomic data into biogeochemical models and unleash the full potential of this wealth of information for understanding and predicting Arctic Oceanic processes. Specifically, the CHANGE project will focus on developing an innovative model that integrates genomic, biogeochemical, and environmental data gathered from long-term Arctic monitoring projects. The project team has been involved in these monitoring efforts since 2015. This model will be dedicated to predicting shifts in the nitrogen cycle in the Arctic Ocean within the context of the current climate change scenario. There is already a 3D coupled physicalbiogeochemical model of Kongsfjorden (SVALBARD Arquioelgo Fjord) implemented by the PI of the project. This model includes the nitrogen biogeochemical cycle and will serve as a “lab bench” to test the new parameterizations developed within CHANGE. We believe that by incorporating genomic information, our new modle will capture the genetic diversity and metabolic capabilities of marine microbes, leading to more accurate predictions in quantifying the effects of ongoing warming on the biogeochemical cycle of nitrogen in Arctic coastal systems. This is a fundamental step towards better constrained forecasts of the Arctic future. The goal of CHANGE is challenging, due to the scarcity of adequate data, translating genetic information into model parameters that seamlessly integrate into established modeling frameworks. In CHANGE project we will achieve this necessitates thorough implementing several historical data analysis and dedicated in situ and lab experimentations to link microbial taxonomy, individual genes and metabolic pathways with biogeochemical activity processes. From this data analysis and experimental activities we expect to identify multiple relationships between genomic taxonomic (metabarcoding) and functional (metagenomics) with biogeochemical (inorganic nutrients distribution, N-Cycle metabolic rates) and environemntal data that will serve as the translated algorithmes to convert Omics data into the biogeochemical modle. We will use various methods to obtain estimates of abundance/biomass of the specific taxonomic microbial groups involved in the N-Cycle, like quantitative reverse transcription polymerase chain reaction (RT-qPCR), metagenomics and metatranscriptomics to obtain biomass proxies from water and from sediment samples. Moreover, we will combine the above methods with measurements of processes associated with the biogeochemical cycle of nitrogen such as ammonia oxidation, nitrification, and denitrification. Finally, we will integrate the acquired data and knowledge in a model that will assist in testing the response of nitrogen-cycle associated organisms to environmental factors. Therefore, CHANGE will rely on field, laboratory, and modelling work.