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These are the intended use-case/justification for one or multiple variable groups. Opportunities are linked to relevant experiment groups. Identifying opportunities helps to provide a structure to map variables against requirements. Each opportunity description will convey why this combination of variables and experiments is important and how they contribute to impact.
| Attribute | Value |
|---|---|
| description | The Coordinated Ocean Wave Climate Project (COWCliP) is an initiative that addresses the critical need for detailed, accurate modelling of wind-driven ocean surface waves. In our rapidly changing climate, understanding how these waves evolve on global and regional scales is essential for predicting coastal hazards, erosion, and other wave-related impacts and supporting offshore renewable energy activities. To achieve this, we need high-resolution wind and sea-ice concentration variables in the CMIP7 dataset. These variables are foundational to accurately model the dynamics of wind-driven waves and their impacts on coastal environments, with significant societal and scientific benefits. The societal benefits of improved wind wave modelling extend far beyond academia. Coastal hazards driven by wind waves pose significant risks to coastal communities, economies, and ecosystems. Currently, around 40% of the global population lives within 100 kilometres of the coast, which equates to more than 3 billion people. This population is projected to increase, further intensifying the vulnerability of coastal areas to hazards such as storm surges, flooding, and erosion. In terms of economic risk, coastal areas are home to major cities, critical infrastructure, and industries like shipping, fisheries, and tourism. According to the World Bank, more than $10 trillion in assets are at risk from coastal flooding and sea level rise by 2050. As climate change intensifies, the frequency of extreme wave events is likely to increase, putting more lives and assets in danger. Additionally, the increasing renewable energy activity (wind, wave) in our seas requires improved wind wave modelling to ensure safety and assess risk during construction and maintenance. For effective wind wave climate modelling, high-resolution temporal and spatial data are essential. Wind patterns can vary significantly over short time scales and distances, and the interaction between wind, waves, and ice requires detailed data to capture these dynamics. CMIP7’s provision of high-resolution wind fields would allow for a more accurate representation of the forces driving ocean waves. Similarly, sea-ice concentration data is critical for understanding how ice attenuates or reflects wave energy, as well as how changing ice cover due to warming temperatures alters wave patterns. Without high-resolution data, models may miss localized phenomena such as extreme wave events or changes in coastal wave energy distribution, which are crucial for understanding and predicting coastal erosion and other hazards. By incorporating finer-scale data into wave models, we can better understand the spatial variability of wave energy, identify vulnerable coastal zones, and assess the future risks posed by changing wave climates. |
| desirable_ensemble_size | 10 |
| expected_impacts | Wind wave spectral numerical models: These variables are essential inputs for numerical models that simulate wind-wave generation, propagation, and transformation. They are used to derive future projections of ocean wind waves under different climate scenarios. Wave climate variability and trends: Surface wind speed and sea ice concentration data can be used to analyze historical and projected trends in wave climate, identifying regions with increasing or decreasing wave energy, which can influence coastal erosion and flooding patterns. Storm surge and extreme event analysis: High-resolution wind data are critical for simulating extreme wave events, storm surges, and their coastal impacts, aiding in disaster preparedness and risk assessments. Polar regions and sea ice dynamics: In polar regions, sea ice concentration plays a crucial role in modulating wave energy. Ice cover reduces wind-wave generation, but as sea ice declines due to climate change, new wave fields can develop, impacting coastal and offshore environments. Wave energy and renewable energy assessments: Wind speed data are used in assessing the potential for wave energy as a renewable resource, identifying suitable locations for wave energy farms based on long-term wave climate data and ensuring appropriate safety and risk assessment during offshore construction and maintenance. Ecosystem impacts: The interaction between wind-driven waves and coastal ecosystems (e.g., coral reefs, mangroves) can be studied using these variables to understand how changes in wave dynamics affect ecosystem health and coastal protection. Ship routing and maritime operations: Future projections of wind wave conditions, based on surface wind data, can inform safer and more efficient ship routing, reducing the risks of dangerous wave conditions at sea. |
| justification_of_resources | - Critical to Global Coastal Hazard Assessment - Foundational for Wind-Wave Models - Critical for Polar and Ice-Affected Regions - Supporting risk assessment for offshore renewable activity |
| lead_theme | Ocean & Sea-Ice |
| minimum_ensemble_size | 1 |
| name | Advancing Wind Wave Climate Modelling for Coastal Zone Dynamics, Impacts, and Risk Assessment |
| opportunity_id | 24 |
| technical_notes | For this opportunity our priority is on temporal and spatial resolution of the data with the scenario extension as a second order priority |
| data_request_themes | Impacts & Adaptation, Atmosphere, Ocean & Sea-Ice |
| experiment_groups | scenarios_extensions, deck, scenarios, historical |
| mips | |
| time_subsets | 80ac3156-a698-11ef-914a-613c0433d878 |
| variable_groups | baseline_daily, baseline_monthly, baseline_subdaily, cowclip_wind_wave_variables, seaice_state_daily_basic |