data/guanacoSummaryOutput.txt

The main idea of the paper is to use a type of artificial intelligence called a "convolutional neural network" to upscale or "super resolve" the resolution of a computer model of the Earth's climate. The authors call their network FSRCNN, which stands for "fast super resolution convolutional neural network".

The Earth's climate is modeled using a computer program called an ESM, which stands for "earth system model". ESMs are very complex programs that try to simulate how the different parts of the Earth system, such as the atmosphere, oceans, and land, interact with each other. However, ESMs are often limited in the resolution they can simulate. This means that they can only model a certain number of grid cells or pixels, and each cell or pixel represents a small area of the Earth.

The authors of this paper wanted to use ESMs to study how the Earth's climate might change in the future. To do this, they needed to upscale the resolution of their ESMs so that they could study smaller-scale features, such as the behavior of individual clouds or the impact of localized changes in the climate.

The authors developed FSRCNN to do this upscaling. FSRCNN is a type of neural network that is trained to learn how to upscale images.