Abstract

This paper seeks to address many questions on climate change. Some questions may be deemed answered conclusively already, such as are we as humans changing the global climate? More interestingly perhaps, this paper seeks to establish conclusive causal links between climate change and its effects, in particular the large wildfires that swept the United States and Canada as well as the bush fires that ravaged Australia in the lead-up to the covid pandemic. There are many ways to build climate models. Ours are built using a bespoke Artificial Intelligence (AI) pipeline. This AI pipeline was built to model the complexities of our global climate and the many noisy interactions within it. A key goal of our model has been to be accessible. This means we have tried to show trends and probable outcomes in the lifespan of the average person, built on data inputs of the lifetimes of present-day generations. People cannot relate to the medieval warm period. They were not there. Instead, we aim to build models from what is and has been playing out right in front of our own eyes. The aim has been to build actionable data within the horizon of decision makers of around 5 to 10 years—or one or two election cycles. How often have we heard about projections for the end of the century? Once we get there, it will be too late. What is needed are models for the “here and now”. And based on our models, individuals might choose where to purchase a house, what country to live in, or fire fighters might direct their resources to better combat wildfires. We have combined our global climate model with city level data across four countries: Australia, the United States, Canada and New Zealand.

Keywords: Climate change, Wildfires, Mechine learning, Data sets