OCEAN MODELLING

Scope & Guideline

Shaping the Future of Ocean Systems Research.

Introduction

Welcome to the OCEAN MODELLING information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of OCEAN MODELLING, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN1463-5003
PublisherELSEVIER SCI LTD
Support Open AccessNo
CountryNetherlands
TypeJournal
Converge1988, 1994, from 1999 to 2024
AbbreviationOCEAN MODEL / Ocean Model.
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address125 London Wall, London EC2Y 5AS, ENGLAND

Aims and Scopes

The journal 'OCEAN MODELLING' focuses on advancing the understanding of ocean dynamics through the development and application of numerical models. It encompasses a broad range of topics related to physical, biogeochemical, and ecological aspects of ocean systems, emphasizing innovative methodologies and interdisciplinary approaches.
  1. Numerical Ocean Modelling:
    The core focus of the journal is on the development and application of numerical models to simulate oceanic processes. This includes the modeling of ocean currents, waves, tides, and interactions with the atmosphere.
  2. Biogeochemical Processes:
    Research on the interplay between physical ocean dynamics and biogeochemical cycles is a significant aspect. This includes studies on nutrient cycling, carbon fluxes, and the impacts of ocean chemistry on marine ecosystems.
  3. Climate Change Impacts:
    The journal frequently publishes studies examining the effects of climate change on ocean systems, including sea level rise, ocean warming, and changes in ocean circulation patterns.
  4. Data Assimilation Techniques:
    A strong emphasis is placed on data assimilation methods that integrate observational data into ocean models to improve predictive capabilities and model validation.
  5. Machine Learning Applications:
    Emerging methodologies involving machine learning and artificial intelligence are increasingly featured, showcasing their application in enhancing model accuracy and forecasting capabilities.
  6. Coastal and Estuarine Dynamics:
    Research focusing on coastal and estuarine environments, including storm surge modeling, sediment transport, and coastal ecosystem interactions, is prominently represented.
  7. Interdisciplinary Approaches:
    The journal encourages interdisciplinary research that combines oceanography with atmospheric sciences, hydrology, and environmental science to address complex oceanic challenges.
Recent publications in 'OCEAN MODELLING' highlight several emerging themes that reflect the evolving landscape of oceanographic research. These trends indicate a growing interest in applying cutting-edge technologies and addressing pressing environmental issues.
  1. Advanced Machine Learning Techniques:
    The use of machine learning and artificial intelligence in ocean modeling is rapidly increasing. This includes applications for predicting ocean dynamics, optimizing model parameters, and assimilating observational data.
  2. Multi-Scale Modeling Approaches:
    There is a trend towards multi-scale modeling, which integrates various spatial and temporal scales to provide a more comprehensive understanding of ocean dynamics and their interactions with climate systems.
  3. Impact of Marine Heatwaves:
    Research focusing on marine heatwaves and their ecological and biogeochemical impacts is gaining prominence, reflecting the growing concern over climate change effects on marine environments.
  4. Coupled Ocean-Atmosphere Models:
    Studies integrating ocean and atmosphere modeling are on the rise, emphasizing the importance of understanding coupled systems in predicting weather patterns and climate variability.
  5. Coastal Resilience and Adaptation Strategies:
    Increasingly, papers are addressing coastal resilience and adaptation strategies in response to climate change, highlighting the need for sustainable management of coastal resources.
  6. Ocean Data Assimilation Innovations:
    Innovations in data assimilation techniques are trending, with a focus on improving model accuracy and predictive capabilities through the integration of diverse data sources, including satellite observations.

Declining or Waning

While 'OCEAN MODELLING' continues to thrive in various research areas, some themes are becoming less prominent in recent publications. This may reflect shifting research priorities or advancements in methodologies that render earlier approaches less relevant.
  1. Traditional Hydrostatic Models:
    There appears to be a decline in the publication of studies solely relying on traditional hydrostatic models, as newer, more sophisticated modeling techniques that capture non-hydrostatic effects gain traction.
  2. Basic Wave Theory Applications:
    Research focused on basic wave theory without integration into more complex models or real-world applications is less frequently represented, indicating a shift towards more applied and interdisciplinary studies.
  3. Static Parameterizations:
    Static parameterization methods in ocean models are being phased out in favor of dynamic and adaptive approaches that better account for variability in ocean conditions.
  4. Standalone Empirical Models:
    There is a noticeable reduction in standalone empirical models that do not incorporate physical principles or data assimilation, as the community increasingly values integrated and data-driven approaches.

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