Climate research simulation

Climate research simulation (CRS) technologies are used to produce virtual visual representations of the weather, helping forecast and understand better how weather patterns are changing as a result of global warming.

CRS technologies use AI models trained on large amounts of data including historical weather data, atmospheric data, pollution indicators and energy usage in private households. The data is used to show how climate patterns have changed over time and project what they may look like in the future. CRS technologies help predict the impact of climate change on a global and local scale. 

CRS technologies are developed by technology companies, such as DeepMind and Microsoft, through open-source AI models, to make weather and flood forecasting faster, more accurate and more accessible. Google Research, for example, uses rivers’ water level data to predict which areas will be most affected in case of flooding. 

Governmental organisations and weather agencies are incorporating CRS into their models to predict extreme weather events in specific parts of the world and their impacts on the local infrastructure, farming and healthcare.

Simulation technologies

Simulation technologies run on AI models that draw on massive historical data sets to create realistic virtual simulations of what a certain place, geological phenomenon, etc. looked like in the past and how it might look like in the future.

What are the benefits of this technology?

CRS saves time and resources, as it is faster at predicting the weather than traditional forecasting methods. For example, GraphCast is able to produce accurate weather forecasts for the next 10 days in under one minute. Faster knowledge on when and where a flood will occur can help make sure that people are safe at the time of the event.

CRSs also contribute to climate change research in general. Through precise virtual simulations, they help study places and events that would otherwise be difficult or impossible to access. For example, researchers can use them to study Arctic sea ice loss or assess the dangers of climate change to human safety and national security.

What are the risks of this technology?

There are concerns that predicting future weather trends based on historical data could be inaccurate and misleading at a time when climate change is causing unexpected climate events.

Another concern involves the environmental impact of AI. Studies have shown that training and operating AI models uses a lot of energy and relies on hardware that emits a significant amount of greenhouse gases. Therefore, although the use of AI has the potential to contribute to climate change research, its very application simultaneously contributes to pollution and global warming.