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Operationalizing climate risk in a global warming hotspot
Climate change is a looming threat to marine life, creating an urgent need to develop climate-informed conservation strategies. The Climate Risk Index for Biodiversity was designed to assess the climate risk for marine species in a manner that supports decision-making. Yet, its regional application remains to be explored. Here, we use it to evaluate climate risk for ~2000 species in the northwest Atlantic Ocean, a marine warming hotspot, to explore its capacity to inform climate-considered fisheries management. Under high emissions, harvested species, especially those with the highest economic value, have a disproportionate risk of projected exposure to hazardous climate conditions but benefit the most from emission mitigation. By mapping critical risk areas for 90 fish stocks, we pinpoint locations likely to require additional intervention, such as in the southern Gulf of St. Lawrence for Atlantic cod. Finally, we demonstrate how evaluating climate risk geographically and understanding how it arises can support short- and long-term fisheries management and conservation objectives under climate change.
Delivering sustainable climate action: reframing the sustainable development goals
Globally, climate change represents the most significant threat to the environment and socio-economic development, endangering lives and livelihoods. Within the UN’s current 17 Sustainable Development Goals (SDGs), climate action is explicitly covered under Goal 13, “to take urgent action to combat climate change and its impacts”. This perspective considers how to re-frame the SDGs and their successor towards mainstreaming climate action within the targets and indicators of all the development goals.
Video communication, blue marble awe, and attitudes toward climate change and renewable energy
We conducted a survey experiment to examine how respondents’ attitudes toward climate change and renewable energy are affected by six communication approaches using online videos. Three interventions used single message approaches, which focused on facts about climate change science, facts about solar PV technology, or the emotion of blue marble awe (the feeling of awe for the Earth arising from the realization that we live on a fragile planet). Two interventions used dual message approaches, which combined blue marble awe with either climate change science or solar PV technology facts. One intervention used a dual reinforced message approach, which combined blue marble awe, solar PV technology facts, and a message from an astronaut who is an ambassador for renewable energy. Results show that the dual reinforced message approach has the strongest effects on energy and environmental attitudes. Our findings offer important lessons for scientists and educators interested in energy communication.
Machine learning map of climate policy literature reveals disparities between scientific attention, policy density, and emissions
Current climate mitigation policies are not sufficient to meet the Paris temperature target, and ramping up efforts will require rapid learning from the scientific literature on climate policies. This literature is vast and widely dispersed, as well as hard to define and categorise, hampering systematic efforts to learn from it. We use a machine learning pipeline using transformer-based language models to systematically map the relevant scientific literature on climate policies at scale and in real-time. Our “living systematic map” of climate policy research features a set of 84,990 papers, and classifies each of them by policy instrument type, sector, and geography. We explore how the distribution of these papers varies across countries, and compare this to the distribution of emissions and enacted climate policies. Results suggests a potential stark under-representation of industry sector policies, as well as diverging attention between science and policy with respect to economic and regulatory instruments.
Compound coastal flooding in San Francisco Bay under climate change
The risk of compound coastal flooding in the San Francisco Bay Area is increasing due to climate change yet remains relatively underexplored. Using a novel hybrid statistical-dynamical downscaling approach, this study investigates the impacts of climate change induced sea-level rise and higher river discharge on the magnitude and frequency of flooding events as well as the relative importance of various forcing drivers to compound flooding within the Bay. Results reveal that rare occurrences of flooding under the present-day climate are projected to occur once every few hundred years under climate change with relatively low sea-level rise (0.5 m) but would become annual events under climate change with high sea-level rise (1.0 to 1.5 m). Results also show that extreme water levels that are presently dominated by tides will be dominated by sea-level rise in most locations of the Bay in the future. The dominance of river discharge to the non-tidal and non-sea-level rise driven water level signal in the North Bay is expected to extend ~15 km further seaward under extreme climate change. These findings are critical for informing climate adaptation and coastal resilience planning in San Francisco Bay.
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