And, of course, scientists are constantly testing how their models perform as new data come in and trying to improve the fit of the models’ predictions to reality. The story of continuous improvements in weather modeling really drives this idea home. For most of human history, people could not travel faster than the weather. The only way to predict what might be coming was to keep careful track of past years to estimate when it might be warm enough to plant crops or dry enough to harvest wheat. Farmers’ almanacs were the best weather model available! Think about how often you check your phone to see whether the weekend will be sunny. Now imagine what it was like to have no warning of an oncoming blizzard or blistering heat wave. With the advent of the telegraph, it became possible to keep track of the weather hundreds of miles away — according to this wonderful story in The New Yorker, early telegraph operators noticed that if the lines were down (suggesting wet weather) to their west, they could expect rain in a few days. These days, of course, weather models are informed by tens of thousands of measurements on the ground, in the air, and from space, continuously updating and correcting for actual conditions on the ground. Today’s three-day weather forecasts are more accurate than 24-hour forecasts were just 40 years ago.
All this to say that models can be incredibly useful and vitally important. But that requires a long-term investment in data collection and analysis (see Spencer Weart’s recent discussion of the dedicated climate researchers at Vostok Station in Antarctica) and the same kind of competitive scrutiny that characterizes all of science.
When you think about models in this way, I think you’ll have no problem seeing through some of the more superficially reasonable-sounding criticisms you’ll hear about climate modeling, some of which have even been leveled by scientists who should know better. Climate models, like weather models, have been continuously tested and refined for more than a half century. Increasingly sophisticated and detailed measurements can now be incorporated and the models’ predictive capabilities have been matched up against reality for decades, proving, sadly, that early estimates of the impact on our climate of rising carbon dioxide levels were, if anything, rather more optimistic than alarmist. In short, the climate models developed and refined by international teams of scientists are sophisticated, complex, and under constant careful scrutiny.
Nevertheless, there are plenty of people who have, for one reason or another, decided that climate change is a hoax or that climate scientists are conformist sheep. These climate deniers are fond of suggesting that climate models are just examples of “garbage in, garbage out” that are designed to support the views of environmental alarmists. For example, in 2007, Freeman Dyson, a brilliant scientist and writer who passed away last year at the age of 96, and who absolutely should have known better, complained: “[Climate models] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observed data. But there is no reason to believe that the same fudge factors would give the right behaviour in a world with different chemistry, for example in a world with increased CO2 in the atmosphere.”
Dyson’s argument is a classic case of exploiting the misconception that all models are just a series of guesses that can be manipulated to give whatever answer you want. While some models might be quick-and-dirty estimates that should be taken with a grain of salt and assessed with a gimlet eye for possible bias, that’s not the case for all models. If someone is going to take aim at international models developed cooperatively and successively refined by hundreds of scientists from dozens of disciplines, don’t let them get away with hand-waving about fudge factors. Everybody agrees that no model is perfect, but nobody should agree that no models can be trusted, and specific criticisms, not general skepticism, are necessary to challenge a specific model.