The Canada Coastal Zone Information System for Model-Based Projections of Future Metocean Parameters from Coupled Atmosphere-Ocean Models Under Different Greenhouse Gas Emission Scenarios for Offshore Marine Energy Development in Canada
Bibliographic record
Abstract
Planning is essential to navigate the challenges and uncertainties posed by climate change as offshore marine energy development proceeds over the coming decades. This paper introduces the prototype version of the Canadian Coastal Zone Information System (CCZIS), a pioneering initiative developed jointly by ASL Environmental Sciences Inc. and Trailmark Systems Inc. through the Innovative Solutions Canada Challenge administered by Public Services and Procurement Canada (ISC, 2020). The core functionality of CCZIS lies in its ability to provide spatial-statistical representations of key metocean parameters such as water levels, waves, sea ice conditions, vertical allowances, and marine winds. One of CCZIS's ground-breaking features is its integration of regional model-based projections derived from coupled atmosphere-ocean models such as The Coupled Model Intercomparison Project Phase 5 (CMIP5). CMIP5 is a collaborative international effort that involves a collection of climate models used to simulate and project the Earth's climate system (Taylor et al., 2012; IPCC, 2013). CMIP5 was coordinated by the World Climate Research Programme (WCRP) and facilitated the comparison of climate models from different global institutions. It served as a framework for assessing the performance of these models, advancing our understanding of climate processes, and providing projections for future climate conditions. CMIP5 models simulate a range of climate variables, including temperature, precipitation, sea ice extent, and atmospheric circulation. These simulations help scientists and policymakers explore potential future climate scenarios under different greenhouse gas emission scenarios. CMIP-5 model realizations for different relative concentration pathway scenarios (RCP) contributed to the Intergovernmental Panel on Climate Change (IPCC) assessments, providing valuable data and insights that inform climate research, impact assessments, and policy decisions. Spanning a variety of greenhouse gas emission scenarios, from the conservative RCP 2.0 to the more extreme RCP 8.5, these projections enable users to toggle between different climate change scenarios with the number representing the increase in net surface radiative forcing. This functionality allows for comparative analysis against metocean design criteria used in past projects against different potential future climate change scenarios, thereby allowing for the assessment of expected metocean extremes under each RCP scenario. A variety of other data sources were reviewed such as the Canadian Extreme Water Level Adaptation Tool (CAN-EWLAT) (Greenan, 2022), MSC-50 (Swail et. al., 2007), etc., and are described further in the analysis section. CCZIS displays three-dimensional bathymetric and infrastructure data together, through the combination of several data sources: high-resolution Canadian Hydrographic Service (CHS) Non-Navigational (NONNA) bathymetry (CHS, 2023) dredging survey data, seabed properties (borehole data), and its support for the geo-referenced three-dimensional display of present and future coastal and offshore infrastructure. In addition, CCZIS has a built-in user input-driven computational tool for computing nearshore waves, for large marine wind events, at any selected location. The integration of hydrographic data, seabed properties, and existing infrastructure with hindcast and future-looking metocean conditions offers a unified data fusion platform to ensure resilient engineering of offshore marine energy installations, including wind farms, energy platforms, transmission infrastructure, as well as ports and small craft harbors where support vessel operations will be based.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".