The recent publication of the sixth assessment report from the Intergovernmental Panel on Climate Change (IPCC) summarizes the current understanding of climate science in unprecedented detail.
The extensive evidence feeding into the report includes observations collected from across land, ocean and atmosphere, as well as numerous simulations from the latest generation of climate models.
However, in recent years, climate scientists have another tool available to them thanks to rapid advances in the development of artificial intelligence (AI) and, particularly, machine learning. In contrast to models that follow a set of explicit and pre-defined rules, machine learning aims towards building systems that can learn and infer such rules based on patterns in data.
As a result, a new line of climate research is emerging that aims to complement and extend the use of observations and climate models. The overall goal is to tackle persistent challenges of climate research and to improve projections for the future.
In our perspective paper, published in the journal Nature Machine Intelligence, we assess current limitations and recent advances of climate models and machine-learning approaches in Earth system science.
We propose an approach whereby machine learning and climate models are not used as individual tools, but rather as connected “hybrids” that are capable of adaptive evolution and self-validation, while still being able to be interpreted by humans.
Continue reading: https://www.carbonbrief.org/guest-post-how-artificial-intelligence-is-fast-becoming-a-key-tool-for-climate-science
The extensive evidence feeding into the report includes observations collected from across land, ocean and atmosphere, as well as numerous simulations from the latest generation of climate models.
However, in recent years, climate scientists have another tool available to them thanks to rapid advances in the development of artificial intelligence (AI) and, particularly, machine learning. In contrast to models that follow a set of explicit and pre-defined rules, machine learning aims towards building systems that can learn and infer such rules based on patterns in data.
As a result, a new line of climate research is emerging that aims to complement and extend the use of observations and climate models. The overall goal is to tackle persistent challenges of climate research and to improve projections for the future.
In our perspective paper, published in the journal Nature Machine Intelligence, we assess current limitations and recent advances of climate models and machine-learning approaches in Earth system science.
We propose an approach whereby machine learning and climate models are not used as individual tools, but rather as connected “hybrids” that are capable of adaptive evolution and self-validation, while still being able to be interpreted by humans.
Continue reading: https://www.carbonbrief.org/guest-post-how-artificial-intelligence-is-fast-becoming-a-key-tool-for-climate-science
