What's the climate like outside today?

By Ben Brown-Steiner

Winter is coming. We all know this. But let’s say that I didn’t know this. What could you do to convince me that winter is coming? If we went for a walk outside what things could you use as evidence?

You could use the colors of the leaves and a description of the seasonal cycle of trees. You could point out the frantic behavior of squirrels and an explanation of what they are burying and why. Both of those things are individual pieces of evidence and a logical explanation of how they fit into a larger pattern, and as such they are pretty convincing.

If it was a cold day you could talk about the decreasing temperatures and talk about how you wished it was still August. That would be pretty convincing. But what if it was an unusually warm day? You would be forced to tell me to forget about the day’s weather and focus on the longer trend. You would tell me to ignore what appears to be counter evidence for your claim. If I were skeptical that winter was actually coming, I may not believe you.

Looking for evidence in an individual day for a change in season, especially if you are talking about temperatures, is really tricky because all we can experience when we step outside is the weather. If I wanted to know what the weather was today you could respond like Calvin’s mother does to his question:

Weather is the day-to-day, hour-to-hour, minute-to-minute fluctuation of the atmosphere we all live in, which is chaotic and highly variable. In order to perceive a seasonal change we need to pay attention to the moving average of daily temperatures over the span of weeks or months. This is because ultimately a change in seasons is not weather. It’s a change in the long-term average temperatures that follows an annual cycle dictated by the tilt of our planet. This cycle is so consistent that during certain times of the year we all expect to see evidence of the change in seasons and therefore we perceive changes in temperatures as evidence of a change in season. During the autumn, it is very easy to experience a cold day and feel that winter is near, or experience a warm day and lament the passing of the summer. But our day-to-day experiences are moments in the constantly fluctuating and highly variable weather. On their own they are not evidence of a change in seasons.

To understand the seasonal cycle, or to understand any long-term average, we have to rely on observations, data, statistics, and pattern recognition. This is especially true if we’re trying to understand the climate. This is because, even more so than the seasons, climate is a long-term average (years to decades) of the weather and averaging is a statistical tool that is abstracted from the weather that we experience every day. For instance, if the high today is 77°F and the low tonight is 35°F, the 24-hour average temperature is around 56°F, which is a poor representation of the hourly temperatures that we actually feel on that day. Even an average of temperatures over a single day is abstracted from real-world experience and is about as useful as that broken clock that is right twice a day.

To get an intuitive feeling for how abstract climate really is, let’s look at Decembers in Ithaca over the past ten years. The following figure is the daily maximum temperature (red), minimum temperature (blue) and average temperature (green) for December 2013.

December of 2013 felt like a weird one. It dropped below 0°F on December 17^th only to reach above 65°F on the 23^rd. What can we predict about this December based on last December? The unfortunate answer is: not much. It would be foolish to predict that this December would match any of the specific highs and lows from last December. Weather is highly variable. What if we look at the average climate for Ithaca in December? The following figure is the same as the one above but with the climate average for each day in a purple line.

The December climatology shows that, on average, the mean temperature drops from 35°F on December 1^st to 25°F by December 31^st. Interestingly, last December looks nothing like the climatology. Why is that? How can we say that we would expect any given December to be like the December climatology when last December was nowhere near the climatology? Was last December an unusual December? We can’t answer these questions based on any single December, so let’s look at the past ten Decembers plotted in the following figure. 

Trying to pick a ‘normal’ December is difficult. They generally show decreasing temperatures, although not always. Temperatures actually increased throughout December in 2007. None of them actually match the December climatology. They all show temperature fluctuations, but the fluctuations don’t really show much of a predictable pattern. If we were to make a statement about the weather for this coming December, all we could really do would be to state the climate average (“Decreasing daily average temperatures from 35°F to 25°F…”) plus make a statement about the average temperature fluctuations and their frequency (“...with deviations from that trend around 10°F every 5 – 10 days”). That’s a climate forecast. It’s not a weather forecast.

We’re back to that fundamental difference between weather and climate. The climate average is so abstracted from our actual experience that it’s impossible to feel the climate in any meaningful way. All we feel is weather. It’s only with statistics and averaging that we can experience climate. To get a weather forecast for December, we’ll have to wait until late November when actual weather models can start to make meaningful weather forecasts.

The following four figures should help us get a more intuitive understanding of these differences. They are the averages of the last 2, 5, 10, and 20 years of daily December temperatures. At only two years, you can still see the influence of the warm days from 2013. Early December in 2012 was also warm, and a 2-year average still is subject to the random patterns of weather. We’re not yet looking at climate.

At five years, much of the year-to-year variability is smoothed out.

At 10 years, the temperature plots are even smoother. It’s looking more like the climatology now.

At 20 years, it looks even better. The green line (average daily temperature) lines up pretty closely with the long-term climatology.

But by the time we’ve averaged 20 years together, we’ve moved from the concrete world of daily weather that we can experience into the abstracted world of climate averages. As we increase the amount of time we’re averaging together, we see less of the impact of weather variability and more of the average abstracted climate trend. It’s not until we’ve averaged at least 20 years together (and many climate scientists would rather average 30 or more years together) that we can even talk about climate.

When anyone talks about climate, or a change in the climate, they are by definition not talking about weather. It’s important to recognize that a single weather event may be extremely warm, cold, wet, or dry, but that event cannot be used on its own as a line of evidence in a conversation about climate. To talk about climate you must talk about abstracted averages of weather that cannot be interpreted or perceived as a thing that we can directly experience.

So next time you find yourself in a conversation that confounds weather and climate, remember that the two things are not interchangeable. Instead of responding with confusion to a question like:

Respond with a brief explanation of the differences, perhaps utilize some statistics, and then go outside and enjoy the weather.

The Climate System as a Foreign Sports Car

by Ben Brown-Steiner

Imagine the earth’s climate system as a foreign sports car owned by a playful friend. Your friend is happy to let you look and listen to the car and will on occasion give you a ride in it. But he’s unwilling to let you look under the hood or study the user’s manual. If you want to try and understand how the car works, and maybe even build your own, you are going to need to be clever and use all the tools at your disposal to figure out how this car works.

This is very much like how earth scientists try to understand the real world. The real world, even more so than the foreign sports car, is very complex and intricate. We are unable to look under reality’s hood or read reality’s users manual. What we are able to do is to make careful observations, come up with theories as to how the real world works, test our theories with experiments and create models to test and explore our understanding.

When you start to build your own car, the first thing you are going to gather are the major parts: a frame, an engine, wheels, rods, doors, spark plugs, fuel and so on. For the Earth’s climate, instead of parts we have variables: ocean temperature, relative humidity, solar radiation, CO2 concentrations, and many others.

However, you need a plan to put your parts together.  You need an understanding of some of the interactions between the parts you have and the function of your car. You know that fuel needs to be injected into the engine in a particular way. You know that the wheels need to be connected both to the engine and to the brakes if you ever want to actually drive your car. Earth scientists know that sunlight heats the surface of the earth, which in turn heats the atmosphere. They know that warm air holds more water than cold air. Any successful climate model needs to have these elementary parts.

To put these parts together you need a design framework. For our car, this is a engineering schematic of the parts and their functionality. We know when brakes are applied, the velocity of the car decreases. For earth scientists, this is typically in the form of code and equations. We know that precipitation falls as rain if the temperature is above freezing and falls as snow if the temperature is below freezing.

During our first attempt, there are many things we don’t know. We know that there is an interaction between the stick shift and the engine. We know that there is some interaction between the oceans and the atmosphere. But we don’t know the exact details. To begin to understand these unknowns we must make careful observations of the thing we’re trying to model and come up with tentative hypothesis and theories. We can listen to the revving of the engine or make observations of ocean and atmospheric temperatures.

You then put together a scheme that you think might work like the real thing. You design some system (or write some code) that combines what you know with the things you have hypothesized. Because you know that you are uncertain about particular interactions, you make those parts easy to observe and easy to modify or swap out with another part. This is what often is described as a parameterization or a scheme in a climate model. It’s a variable or equation that you know you’ll have to tweak or change out later on.

For instance, after you run the car you notice that your car moves backwards when you think it should be moving forwards or your climate snows when it should be raining. You look carefully at your variables and design and equations and parameters and try to find the error. You may have had your wires mismatched our you may have had inaccurately represented the relationship between moisture and temperature.

The next step takes this newfound understanding and incorporates it into your next model. You fix your mistakes and you tune your parameterizations. Then you test it again and repeat the whole process over and over until your understanding grows. Fundamentally, this is is the way that science functions. It is an interactive process. It’s never ending. You test and tune and observe and reformulate and repeat. Eventually, if you are clever and lucky, your model gets better and you gain a deeper understanding.

Unfortunately, since we are not able to look at the actual foreign sports car (because our friend is too secretive) and since we will never see the inner workings of the real world, we are never going to have a perfect model. Models by definition are simplifications of the real thing. You strive to have a really good simplification that provides insight and understanding. And you keep trying. This is science.