"Computer models are unreliable, and can't be used to accurately predict and model climate"
When predicting climate change, computer models are among the most accurate tools we currently possess. While humans are notoriously for finding patterns where they don’t exist, computer models are able to do the opposite, and find patterns that exist, regardless of how apparent they are to us. Furthermore, computer models only get more accurate over time, as they have a larger pool of data to learn against and draw trends from. Financial institutions heavily rely on computer models to predict the action of global markets: for example the Black Scholes formula is a model used to evaluate the price of a stock option based on inputs derived from the market and the specific option, such as volatility and interest rates. If these institutions make actionable decisions on volatile assets such as stock options based off of a mathematical model, than it is rather elementary to suggest that a non-volatile measure such as average global temperature or sea level cannot be modeled by computers using the large amount of data available. As more climate data becomes available to us, climate models will be able to predict future conditions better than they have in the past, and as machine learning technologies progress, they will be able predict climate in ways we cannot understand. These compounding factors, in conjunction with the aforementioned confidence other institutions place in computer models, lead me to believe that in time, computer models will be the most valuable tool we have to model climate.
Reading detailing the widespread applications of modeling, just in the financial sector alone.
Contributed by: Miguel Chavez