The present time sees an acute urgency in climate change action. Across the board in government, business, and community, there is a quest for better and faster climate change solutions. In comes AI-based climate models which are game changers in how we look at, predict, and respond to climate data.
By improving accuracy, speed and the range of what we analyze AI is re-shaping environmental action plans and giving us better tools to fight climate change.
The Evolution of Climate Modeling
Traditional climate models at the time of their development were very innovative but did use large-scale mathematical equations and supercomputers. While very informative, these models had issues with the large-scale interconnection of the Earth’s systems. Also they could take from days to weeks to run and produce forecasts.
AI-powered models in particular which are based on machine learning and deep learning we see a new direction. By using past climate data, satellite images, and real time sensor reports, AI is able to identify the patterns and issues at a much greater speed as well as a higher precision which which traditional systems do not.
Enhancing Prediction Accuracy
AI’s greatest benefit is in improved prediction accuracy. We see that machine learning from large sets of data which we have terabytes of at our disposal, is able to identify trends the human eye may miss. Also for instance, AI is better at predicting the onset of extreme weather like hurricanes, droughts and floods which in turn gives governments and aid organizations that much-needed time to prepare.
Also in that regard AI models are used to present how ecosystems, cities and economies will react to various carbon emission scenarios. This in detail looks at the future and thus enables policy makers to develop very targetted and effective environmental policies instead of to large and general forecasts.
Localized Climate Insights
Environmental plans have to be tailored to each region which is to be expected given that climate impacts are very different by location. AI in this regard is doing what we call hyperlocal climate modeling, which looks at weather trends, land use, and population info in great detail. From a coastal city that is seeing the effects of rising sea levels to a farm community that is dealing with changed rain patterns, AI puts in place very specific, region-based solutions, which in turn are more effective.
Faster and More Dynamic Planning
In a world of rapid climate change, speed is of the essence. Traditional models are often out of date by the time of their release. AI, on the other hand, is able to constantly improve and update its predictions as it gets new data. This live modeling approach which includes continuous improvement of results as they come in sees that environmental action plans are very much in the present and relevant.
Also in which AI is able to run many simulations at the same time thus presenting decision makers with a variety of possible outcomes based on many variables. This flexibility which we see in AI allows stakeholders to better assess risk, cost, and benefit when they are putting together action plans.
Challenges and Ethical Considerations
While AI-based models present a great opportunity, we also see issues. What we put in is what we get out model results are only as good as the input data that they are given. Also, we see that it is an issue of putting too much faith in AI, which in turn diminishes human input and local knowledge.
Transparency and traceability in AI’s role in decision making is also key to gaining the trust of policymakers, scientists and the public. We must see more of a partnership between tech experts, environmental scientists, and community leaders to fully realize the value of AI in climate action.
