In an amusing post yesterday, Dr. Jeff Masters looked at the various forecasts by wooley bear caterpillars regarding the impending winter. The verdict? “Two out of three woolley bear forecasts point to a colder than average winter for the Appalachian region of the U.S.”
But what do the, you know, scientists think? Not just about Appalachia, but about the whole country? Well, NOAA’s seasonal forecast is calling for a warmer-than-average winter across the central United States and Alaska, with an equal chance of warm or cool conditions in the rest of the country.
But Dr. Masters candidly points out that these seasonal forecasts, much like the hurricane seasonal forecasts that I’ve been so critical of, have precious little skill. Indeed, he says, the forecasts’ accuracy is “not much better than flipping a coin.”
Dr. Masters then launches into an excellent explanation of why this does not necessarily mean that long-term climate forecasts are unreliable:
A common complaint one hears about global warming predictions made by climate models is, “How can we trust the predictions of these climate modes, when they so such a lousy job with seasonal forecasts?” It’s a good question, and there is no doubt that seasonal forecasts have pretty marginal skill. However, there is a fundamental difference between making a seasonal forecast and making a 100-year climate forecast. A seasonal or a short-term weather forecast is what mathematicians call an “initial value” problem. One starts with a set of initial meteorological and oceanographic values that specify the initial state of the planet’s weather, then solve the equations of fluid flow to arrive at the state of the atmosphere a few days, weeks, or months into the future. This forecast is highly sensitive to any imperfections one has in the initial conditions. Since there are large regions of the atmosphere and ocean we don’t sample, it’s guaranteed that the prediction will suffer significantly from imperfect initial conditions. Furthermore, the chaotic and turbulent nature of the atmosphere leads to many “bumps” in the weather pattern over time scales of days, weeks, and months. The nature of turbulence makes it impossible to accurately forecast these “bumps” that are superimposed on the mean state of the climate.
A 100-year climate forecast, on the other hand, is what mathematicians call a “boundary value” problem. Given an initial and final set of factors (called “forcings”) that influence the climate, one runs a climate model 100 years into the future. The final state of the climate will depend on the strength of the forcings supplied. This type of model is not very sensitive to initial conditions, and is not trying to forecast the “bumps” of chaotic, turbulent atmospheric motion superimposed on the mean climate. Rather, one is trying to forecast the mean climate. As computer power increases and our physical understanding of how the climate works grows, these type of models will continue to significantly improve. While climate models do fail to properly simulate important aspects of our past climate, such as the Arctic warming of the 1930s, and the observed 0.1°C global temperature increase that occurs at the peak of the 11-year solar sunspot cycle, they have been very successful at simulating things like the global cooling triggered by the 1992 Mt. Pinatubo eruption, and the observed pattern of greatest global warming in the Arctic. I believe that climate models are already significantly more reliable than seasonal forecast models, and should continue to improve steadily in coming years.
This blog is, as I’ve said before, agnostic on the core question of anthropogenic global warming, and on the separate, subsidiary question of what impact such warming will/would have on hurricanes. However, this blog is strongly opposed to illogical arguments by both sides in the global warming debate, and “you can’t trust the climate models because short-term and medium-term forecats are often wrong” is a prime offender in that category. So I appreciate the well-articulated rebuttal.