# Weather Prediction, Climate Prediction. What’s the Diff?

If scientists can’t accurately predict the weather next week or the week after, how can they predict the climate in 10 or 20 years? Good question. The answer lies in apples and oranges.

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Scientifically speaking, the difference between weather prediction and climate prediction is the difference between an “initial value problem” and a “boundary value problem.” Let’s see if I can explain in English.

While weather and climate both focus on temperature, wind, cloudiness, rain or snow, the way these properties are used is quite different. The National Center for Atmospheric Research defines the two like so:

• “Weather is the mix of events that happen each day in our atmosphere.”
• “Climate … is the average weather pattern in a place over many years.”

When Time and Place Are Critical

When you want to know the weather, time and place are critical. You are interested in what is going to happen in the immediate future (not sometime in the next month or two) and in your vicinity (not 1000 miles away).

If the TV weather person announced it was going to rain somewhere in your state sometime next month, I suspect you’d find that prediction a little less than satisfactory. But the latter is essentially what a climate prediction is, and the methodology to arrive at it is fundamentally different from predicting the weather.

What Goes into Weather Predictions

Imagine you are a center fielder on a baseball team. The batter hits a fly ball your way and it’s your job to catch it. To do so, you need to figure where in center field the ball is headed and when it’s going to get there.

If you’re a good outfielder, a crack computer in your brain gathers up essential data -- like the speed of the bat as it hits the ball, the sound of the impact, and the ball’s initial direction -- and in a split second calculates the ball’s trajectory.

But to do this well, it’s essential that the input into your computer – what scientists would call the initial values – is complete and accurate. If the glare of the sun or stadium lights obscure your view of the ball’s initial flight, your ability to accurately predict where the ball is going and when it will get there is impaired.

Predicting the weather is similarly dependent on the initial values you specify in the computer model used to make the prediction. These initial conditions include temperature, wind speed, wind direction, and precipitation rates everywhere in your model – essentially everywhere in the atmosphere. The values for these parameters can’t be made up; they must come from real data. Today these data come from the global meteorological network run by countries around the world and largely coordinated by the World Meteorological Organization. This network includes surface meteorological stations, balloon measurements, shipboard measurements, and space-borne platforms.

Despite its very impressive size, the network is limited; we can only make meteorological measurements in so many locations and these measurements are not perfectly accurate. Thus, the initial conditions input into our weather models are imperfect, and so our weather predictions are inaccurate -- and would be even if our understanding of the physics of the weather were perfect.

Because the effects of imperfect initial conditions on weather simulations tend to grow, the longer the weather model is run into the future, the less accurate the prediction. Predictions of the weather just a week or two in advance, let alone decades, become highly problematic.

What Goes into Climate Predictions

Less concerned with exact time and place, predicting climate focuses on spatially and temporally averaged conditions.

Unlike the earlier example of the outfielder who must know exactly where the ball is heading and when it will get there, climate prediction is more akin to predicting at the beginning of the game how many times a ball will be hit to center field sometime in the first three innings. Initial conditions like the speed of the bat or direction of the ball as it leaves the first batter’s bat are not going to help very much. The critical factors are the speed and direction of the wind, the properties of the ball and the bat, the strength of the pitcher and the batters, and the dimensions of the field – factors that scientists call boundary conditions.

So while predicting the weather depends critically on getting the initial state of the atmosphere right, predicting the climate does not. Which is not to say that climate prediction is easy. It’s not.

Predicting climate accurately depends on getting a host of those boundary conditions correct, many of which relate to the atmosphere’s energy. They include the amount and strength of sunlight reaching the Earth, the reflectivity of the Earth’s surface, the movement of heat in the oceans, and the opacity of the atmosphere to terrestrial radiation as a result of greenhouse gases. And for this reason, getting long-term, accurate observations of, for example, the variations in the sun’s output of energy over time is critical for understanding past climate change. Uncertainties in how the sun’s output will change in the coming decades limits our ability to predict future climate with complete confidence. However, such decadal variations in the sun’s output are irrelevant to predicting tomorrow’s weather.

There are other fundamental differences between weather and climate predictions. Some of these relate to mechanisms. For example, accurate weather predictions require a good simulation of the processes that lead to precipitation from a cloud since whether or not it rains at a specific location on a specific day is relevant. For climate predictions, the specifics of the cloud-to-rain process are less important. Far more important is getting right the reflective properties of the cloud since these affect the planet’s long-term energy budget. Again, both of these inputs present difficult but different challenges.

And that’s why comparing the limitations of weather predictions with those of climate predictions is a little or a lot like comparing apples and oranges.

Bill Chameides
Dean, Duke University
Nicholas School of the Environment
|www.TheGreenGrok.com

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## 11 Comments

I think it's absolutely baffling the amount of arrogance in the man made climate change community. With 30 years of accurate weather data such a large number are prepared to swear under oath that the sky is falling. There are many scenarios and hypothesis for the fluctuations in glaciers and average temperatures that do not include chicken little.

Further I say 30 years of accurate data because, if any self respecting "climatologist" were told to do their job with only equipment available 30 years ago they would say its imposible to accuratly make any assumptions let alone predictions.

But that doesnt seem to raise any eyebrows lately.

This is a very informative article laying out the differences in the problems of prediction between meteorology and climatology.

iraqistan should address his or her arguments to the premises outlined in the article, rather than a generalized rant against "arrogance in the man made climate change community."

The article does not make a case for man made climate change. It simply describes the differences between two separate and distinct disciplines, and why conflating the two is like comparing apples and oranges.

Very good commentary. The computer models used to help predict weather and climate change both rely on understanding and properly characterizing the many mechanisms involved.

As iraqistan points out, there seems to be a much higher degree of certainty about the mechanisms affecting climate change among the most vocal faction of the climate change community. The weather prediction community appears much more reticent to suggest that they have grasped all the complexities of weather modeling.

Why certain members of the climate change community seem so stridently sure of their conclusions is a mystery. Many other scientists studying climate approach their work and conclusions with more caution, possibly because they think there is a lot to still be learned. As Dr. Chameides illustrates, prediction is complex.

iraqistan: it is baffling to me how a rather innocent post on the difference between weather prediction and climate prediction could illicit so much wrath. Although I agree that it is highly unlikely that chicken little is the cause of any climate change, either now or in the past -- even if I had only 30 years of data (actually we have a lot more).

------
Dr. Bill Chameides
Dean, Duke University
Nicholas School of the Environment
www.nicholas.duke.edu | www.TheGreenGrok.com

Twitter: theGreenGrok

Steve - could not have said it better, although I just tried (see above).

------
Dr. Bill Chameides
Dean, Duke University
Nicholas School of the Environment
www.nicholas.duke.edu | www.TheGreenGrok.com

Twitter: theGreenGrok

Apples and oranges but both are fruit. Climate models are still poor predictors probably because they have yet to understand what all the relevant factors are and how they interact, let alone the degree of effect.

Note that models predicted 2007 would be a record warm year when it had dramatic cooling...also the prediction that the Arctic would be ice free by the end of the melt season in 2008 when there was an increase in ice mass the size of Germany. Hansen's GISS website still states (erroneously) that all the hottest years on record were from 1998-2007.

IIRC, Hansen's crew has predicted that this year and/or 2010 will be one of the warmest ever due to the El Nino effect. We'll see how good Hansen's models are.

I apolagize my previous post was off topic. I had read a few articles previously and was skewed. I meant to offend no-one. I understand and realize there are many people doing very dilligent work in this feild and its moving in leaps and bounds. But I do caution making predictions in a feild with infinite variables when there is so much left to learn. weather prediction by comparison is a walk in the park. I beleive personally so much more of the hard work put into this is needed.

laughingboy - it's still apples and oranges. Predicting what global temperatures will be in a year's time is hardly a long-term climate prediction; it is actually a long-term weather prediction and as such is more akin to an initial value problem rather than a boundary value one.

------
Dr. Bill Chameides
Dean, Duke University
Nicholas School of the Environment
www.nicholas.duke.edu | www.TheGreenGrok.com

Twitter: theGreenGrok

Yes. But the bottom line is still the same. The computer models have proven woefully off be they one year, five year or 10 year projections. Hansen's models in 1988 were all off and, with the exception of the El Nino year in 1998, overestimated the actual temps.

If the models were off by a small amount or a consistent amount, then I think your point would be well taken. However, current models are just off by direction and degree. They, or the folks who design them, make many numerous, short-term, specific predictions that just don't come true. That casts doubt on their longer term predictions. And given that the study of the climate is relatively new, at least with the computer models, the predictive value essentially nil.

This actually reminds me of the "Population Bomb" by Ehrlich in the late 1960s/early 1970's. Based on the Malthusian assumption/theory that population grows geometrically and food and other resources grow arithmetically, predicted all sorts of cataclysmic things would happen. They didn't. Resources kept pace with population growth and growth wasn't endlessly geometric but started to decelerate.

I suspect the boundary values are incorrect in the climate models be it Hansen's in 1988 or the IPCC's in 2001 or anything in between. I at least hope some of the Russian climate scientists are wrong. I read some of them are predicting a new ice age.

That fielder and all humans born and raised in a one G field have a distorted understanding of physics.

Take a person raised on the Moon or Mars and catching that ball would be impossible because the ball would not behave the way his instincts tell him it should.

The correctness of a weather forecast can be proven in a couple of days or weeks, so a weather man has to be careful and allow for possibilities not captured by the data he has to work with. Climatologists can promise you anything but in 20 or 30 or 100 years when the changes are supposed to have happened will we remember? Will they still be around to say "I told you so" or "sorry I was wrong"?

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