Climate change is a perennial concern for humanity, but so is the weather. Throughout history, farmers, merchants and soldiers have obsessed about the weather and how it impacts their fortunes. People have kept records to help predict the upcoming year. They built massive observatories to monitor the sun's position. They applied religious beliefs to explain extreme weather (drought, flood, etc.).
In our day, we too insist on predicting the weather. But even with our satellites, weather stations, and computer models, weather forecasts are only useful about one week out.
That doesn't stop the National Weather Service's "Climate Prediction Center" from making long-range forecasts:
The utility of such forecasts is difficult to estimate, but it can't be very significant, except to show that we simply can't forecast the weather.
What does this have to do with climate? Nearly on a daily basis, I encounter AGW proponents who claim people they disagree with "don't understand" the distinction between climate and weather. Here's a new one I saw today:
The author, Steve, is a computer science professor who thinks he understands the arguments made by those who challenge AGW proponents. He offers the valid (even obvious) explanation that weather prediction and climate models have different problems: "one is an initial value problem, and one is a boundary value problem."
But then he makes this ludicrous assertion: "Which also partly explains why a small minority of (mostly older, mostly male) meteorologists end up being climate change denialists. They fail to understand the difference in the two problems, and think that climate scientists are misusing the models. They know that the initial value problem puts serious limits on our ability to predict the weather, and assume the same limit must prevent the models being used for studying climate. Their experience tells them that weaknesses in our ability to get detailed, accurate, and up-to-date data about current conditions is the limiting factor for weather forecasting, and they assume this limitation must be true of climate simulations too.
Let's look at what Steve says about climate models: Climate models have also improved in accuracy steadily over the last few decades. We can now use the known forcings over the last century to obtain a simulation that tracks the temperature record amazingly well. These simulations demonstrate the point nicely.
And now, look at how well the climate models simulate the real world:
So it's not a question of confusing methodology or confusing the problems; it's a question of assessing the utility of the climate models in light of their predictive value. So far, based on their track record, they're not much use at all. And yet, these models are the basis for governments around the world spending billions of dollars to prevent or mitigate "climate change."
In our day, we too insist on predicting the weather. But even with our satellites, weather stations, and computer models, weather forecasts are only useful about one week out.
That doesn't stop the National Weather Service's "Climate Prediction Center" from making long-range forecasts:
The utility of such forecasts is difficult to estimate, but it can't be very significant, except to show that we simply can't forecast the weather.
What does this have to do with climate? Nearly on a daily basis, I encounter AGW proponents who claim people they disagree with "don't understand" the distinction between climate and weather. Here's a new one I saw today:
But then he makes this ludicrous assertion: "Which also partly explains why a small minority of (mostly older, mostly male) meteorologists end up being climate change denialists. They fail to understand the difference in the two problems, and think that climate scientists are misusing the models. They know that the initial value problem puts serious limits on our ability to predict the weather, and assume the same limit must prevent the models being used for studying climate. Their experience tells them that weaknesses in our ability to get detailed, accurate, and up-to-date data about current conditions is the limiting factor for weather forecasting, and they assume this limitation must be true of climate simulations too.
Ultimately, such people tend to suffer from “senior scientist” syndrome: a lifetime of immersion in their field gives them tremendous expertise in that field, which in turn causes them to over-estimate how well their expertise transfers to a related field. They can become so heavily invested in a particular scientific paradigm that they fail to understand that a different approach is needed for different problem types. This isn’t the same as the Dunning-Kruger effect, because the people I’m talking about aren’t incompetent. So perhaps we need a new name. I’m going to call it the Dyson-effect, after one of it’s worst sufferers."
This is typical of the type of argument we see on skepticalscience and other anti-anti-AGW blogs.Let's look at what Steve says about climate models: Climate models have also improved in accuracy steadily over the last few decades. We can now use the known forcings over the last century to obtain a simulation that tracks the temperature record amazingly well. These simulations demonstrate the point nicely.
And now, look at how well the climate models simulate the real world:
So it's not a question of confusing methodology or confusing the problems; it's a question of assessing the utility of the climate models in light of their predictive value. So far, based on their track record, they're not much use at all. And yet, these models are the basis for governments around the world spending billions of dollars to prevent or mitigate "climate change."
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