Assessing technological measures
A comprehensive assessment of 13 categories of ocean-based measures to reduce climate-related drivers was compiled in 2018 by Gattuso et al. (see Figure 1). They assessed such aspects as:
Technological readiness
Duration of the effects
Cost-effectiveness
Disadvantages of the various ocean-based measures.
They concluded the most promising management options were renewable energy and local actions that can be scaled up. Examples of local actions include the restoration and conservation of coastal vegetation, and eliminating overexploitation of living resources and over-extraction of non-living resources. These are all benefits that may be provided by MPAs.
However, all the assessed measures had trade-offs, and there is a need for further research and improved scientific understanding to better inform policy and decision-making.
Figure 1: Potential ocean-based measures to address the causes of climate change. The size of each circle indicates the level of technological readiness; the colour of the circle indicates the duration of the likely effects. Source: Gattuso et al., 2018. CC BY 4.0.
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Downscaling global predictions
Many current climate predictions are based on global projections. However, climate change information at national and local levels is more relevant for individual MPAs. For example, sea levels around Australia are rising faster than the global average (IPCC, 2021), while sea level is predicted to drop slightly in some locations (e.g., the Arctic).
The IPCC Interactive Atlas operates on the latest generation of advanced climate models (CMIP6). This provides exceptional opportunities for understanding the finer-scale impacts of climate change under a range of scenarios.
Two main forms of downscaling are used to translate data from global models into smaller spatial scales:
Limited area modelling (or dynamical downscaling) uses global climate data to drive a regional, numerical model at a higher spatial resolution.
Statistical downscaling establishes a statistical relationship between large-scale variables, like atmospheric surface pressure, and a local variable, like wind speed, at a particular site.
Irrespective of the downscaling method, it’s important to understand the nuances of using downscaled climate data to make decisions.
Climate-engineering: yet to be proven and potentially risky
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