Friday, October 24, 2014

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 17th only to reach above 65°F on the 23rd. 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 1st to 25°F by December 31st. 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.

Friday, October 17, 2014

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.

Monday, September 15, 2014

The Grid Box

by Ben Brown-Steiner

The above image is taken from a popular indie game called Minecraft that gives players free access to an environment with in which they can interact with water, land, lava, and other natural resources. It’s blocky textures are a unique style for modern games, but serves as a wonderful example of how climate modelers look at our world.
The Earth is huge and complex. It’s easy to lose yourself observing the infinite details in an ant colony, a thunderstorm, or a sunset. And while that can be fun, if we’re to understand what’s happening over the entire planet, we need to divide it up into parcels and we need to try and understand how each chunk interacts to its surroundings.
Climate scientists who use models are become comfortable with viewing the Earth as a set of boxes. In order to get our increasingly powerful computers to create realistic simulations of our world, we have to divide up the atmosphere, the oceans and the land into grid boxes. For each grid box the computer tracks a single value for each variable. A grid box has one temperature, one value for cloudiness, one relative humidity, and so on. And a typical climate model has a time step of roughly 20 to 30 minutes.
Currently, we consider a high-resolution global climate model to have boxes that are around 60 kilometers or 35 miles per side. That’s roughly the length of Cayuga Lake. So a global climate model isn’t capable of seeing Cayuga Lake! How is it possible that a grid box that encompasses nearly all of Cayuga Lake and that jumps forward in time every 30 minutes has any chance of being realistic?
It’s because, somehow, astonishingly, the movement of mass and energy in the real world is amazingly organized. To some degree, it’s intelligible, which means that we can study it, take some notes, and understand the basic principles of how the it operates.
            Even though you could be standing in a parking lot in direct sunlight blinded and sweating and someone 100 feet away could be lounging in the breeze under a shady tree, if you average all of the temperatures in a region and track those values over time, you find smooth and regular patterns. If you average every temperature in a day (from the high temperature in the afternoon to the low temperature at night) and draw a graph, you can see the regular and repeating cycle of our seasons.
If you are examining a region’s climate you look at these average cycles. You ask questions like: Are the seasonal highs and lows changing over time? Is it drier, on average, this decade than it was last decade? How does the average state of our region influence the surrounding regions and the global climate? These questions are the questions of climate science and the purpose of these questions is to examine averages. In this sense, a large, abstracted grid box, is a really great way of looking at the big picture.
What, however, if you care about the high temperature tomorrow? If you want to know whether you should bring an umbrella on your walk right now, you do not ask a climate scientist. If you did, you would get a funny look and perhaps a response like this: the average daily high for September in Ithaca is 70 degrees and on average it rains 3.5 inches.
If you ask the question “What’s the weather going to be tomorrow?” you would ask a meteorologist, and the meteorologist, because they don’t have to worry about the entire globe all at once, can zoom in on smaller atmospheric patterns. They can run simulations with time steps as short as 10 seconds and grid boxes as small as one mile (~1.5 km) per side.
To look at a concrete example, let’s say you’re a climate modeler trying to capture interactions between clouds, the Earth’s surface, and solar radiation. You recognize that the real world is complicated and chaotic, but you know that there is some underlying structure that you can model. You take a look at a satellite photo over the ocean and it looks like this (from NASA):


How are you going to recreate this reality in your model? Clearly, you’ll have to simplify. If this particular picture is 100 miles per side, you know that your computers can’t capture that level of detail and have any chance of running on your computer. You need grid boxes. What size grid box is appropriate? If you had the computational power, you could build something like this:


Even though you know you’ll have to make simplifications, and parameterize (we’ll talk about these in another post) some of the small cloud features, you can still represent the overall cloud system you see in the satellite photo. But then your IT staff tells you that there’s no way you can run the model at that resolution. You need to try a somewhat coarser resolution:


You aren’t all that happy with this one since you lose a lot of detail and you’ll have to make different and broader assumptions. For instance, you’ll have to completely forget about resolving individual clouds. You’ll have to start representing cloudiness in a grid box as a percentage (real climate models do this). After some time, you hear from your IT staff that you can run this resolution, but it will take three months to run 1 year of your simulated Earth at this resolution. For your purposes, that’s not practical. Since you don’t want to give up you decide to go to an even coarser resolution with grid boxes of roughly 35 miles per side:


This one leaves out even more detail. You can hardly recognize this as a system of clouds anymore. But in exchange you can run your model at this resolution much more quickly and you’ll be able to examine the details of what you think is going on with much more confidence and data points. Right now, this is the grid box size of many climate models. And even though they use this resolution, they can be used to understand our Earth. The following image is an example of North America viewed through a variety of resolutions used today:


            The top left resolution is a course resolution used in the past. The top right is roughly the resolution of the average climate model today. The bottom left image is roughly the resolution that is considered high resolution today (the resolution that doesn’t quite see Cayuga Lake) and the bottom right resolution is one used more by meteorological models than climate models.
            As we’ve mentioned before, a model by definition is a simplification. It’s not going to simulate reality, and you are going to have to make sacrifices. But if you are careful and you understand what parts of reality you’re ignoring and what parts of reality you’re including in your model, you are able to interpret your results and hopefully discover something new that wasn’t understood before. The history of weather and climate modeling is a wonderful history of practical limitations, amazing ingenuity and cleverness, and glorious tales of scientific advancement of our understanding or our Earth.

Tuesday, September 9, 2014

Blog Post: Why Use Models at All?


By Ben Brown-Steiner

I intend on touching on many topics related to the broad expanse that is climate science, but for a first post I’m going to tackle a question that comes up every once in awhile and probably should come up more often: Why use models at all?

At its purest science is about careful experimentation and observation. We take measurements. We come up with theories. We test those theories. And science gets done. Why bother with complicated models at all?


Well, there are a lot of reasons. The first is that experimentation and observations can be expensive. Or extremely difficult. Or even impossible. We can’t create a second Earth and start tweaking with the climate. No one is advocating that neuroscientists start methodical lobotomies to learn about the brain. And it’s no longer computationally prohibitive to run a meteorological model a dozen times and look at the possible weather patterns in order to make informed decisions about tomorrow’s weather.


Second, as any tinkerer, engineer, mechanic, or chef will tell you, the best way to learn about how something works is to take it apart, look at the individual pieces and how they interact, and put it all back together. Modeling of any type, from simple toy models to expansive climate models, hold at their core this basic mentality.


To introduce the beauty and power of models of all sizes, I’m going to explore a particularly practical type of model: a model intended to help us cook a perfect steak. Apologies if you’re not a meat eater. Just pretend the rest of this post is talking about tofu or seitan.


Before we start, it’s always a good idea to define our terms. For our purposes, a simple and workable definition of a model is: a model is a representation of some aspect of the real world. I like this definition for its simplicity and its brevity. It has three main parts, each of which is important. First, “representation.” A representation is not the real thing, it doesn’t strive to be the real thing. But it does strive to approach the real thing. Any model is going to be a simplification. Second, “some aspect.” A model doesn’t try to represent the entirety of the world around us. A model represents a part of the whole, and often for a particular purpose. Third, “real world.” A model tries to represent some part of the actual real world that we all live in. A model strives to claim some aspect of “reality” and “truth.” These are big, philosophical concepts, but concepts that are at the core of any modeler or programmer’s vision for their model.


So let’s explore various types of models used to cook our perfect steak.



Perfect Steak Model #0:



To really start at the beginning, we should imagine how we would try and cook our perfect steak without any models informing our procedure. We could, perhaps, buy a thousand steaks and randomly toss them on the grill, flip them on occasion, and hope to discern the secret to steak. It’s extremely unlikely that you’ll learn much through this method. Alternatively, we could skip the whole idea of trying to cook steak ourselves and follow a procedure instead.

Perfect Steak Model #1:

So this first model is less of a model and more of a procedure. This procedure goes: go to your favorite restaurant (or friend’s house) and have them make the perfect steak for you. Alternatively, we could describe this procedure more generally: go to an expert and rely upon the expert’s knowledge to produce a perfect steak for you.

Really, this is a great model for the perfect steak. Chefs are culinary experts trained in the alchemical combination of physics, chemistry, thermodynamics, and practical realm of food science. They know how to make a great steak. For our current purposes, however, this is cheating.


Perfect Steak Model #2:

If you happen to enjoy cooking, you probably consider yourself an amateur steak-cooker (or perhaps an expert steak-cooker), and thus have your own procedure for cooking a perfect steak. This procedure, almost certainly, is based off other experts’ procedures which have been simplified for your purposes. For instance, there are many cookbooks with cooking times per side for a perfect steak that probably look something like this (taken from http://www.raysmarketonthecommon.com/):
 

This table is a simple procedure distilled from some expert model (i.e. representation of a real-world steak cooking procedure) prepared for the at-home cook’s needs. This simple table can be further simplified  by the following recipe: “Heat a grill to 350 degrees F. Cook the steak on one side for 3 minutes plus one minute for every quarter inch of thickness over one-half inch. Then flip the steak over and cook the other side for two minutes plus one minute for each quarter inch thickness of the steak greater than one-half inch.” It’s not a graceful recipe. It ignores some of the complexity, but it gets the job done.

The following graph is a more insightful representation of this our Perfect Steak Model #2:



Note that the blue line (for the first side) is quite simple. A straight line like this is called a linear trend. The red line (for the second side), however, is not so simple. It’s not linear, and this non-linearity implies that there is some underlying steak science that has been simplified in this method. 

Perfect Steak #3:

If you are a particularly dedicated at-home cook and was determined to produce the perfect steak, you might create on your own (or stumble upon, like me) this website https://groups.csail.mit.edu/uid/science-of-cooking/home-screen.html, which turns up the complexity to the maximum. This site includes many parameters (which we’ll talk about in a later post) including: thickness, time per side, meat type, starting temperature, and number of sides (i.e. number of times you flip the steak). If you fill out the individual parameters and click on the “cook” button, you’ll get a figure, similar to this one which represents a particular slice of meat and the amount of “doneness” throughout:

Quite quickly you’ll notice the complexity captured by this method:
  • note that the meat keeps cooking for over five minutes after you remove it from the heat.
  • note the percentage of meat that’s “done” for each category: raw, rare, medium rare, medium, well done, browned, and charred) and how complicated the interior of the steak looks.
  • note an option to view the final temperature of each portion of the meat.
  • if you visit the link, you’ll find caveats and sources and alternative methods to compare.
This particular model, developed by the people at MIT for an online class on the science of cooking, has taken into account aspects of physics, chemistry, and food science to develop, parameterize, tune, and code this model. It’s informative, a little absurd, and a great analogy for the complexity possible for any type of system, for any type of model. If you follow this procedure, you can have high confidence that you’ll get your perfect steak.

So now we need to address the question: Which model is the best model?


All three methods above rely on assumptions and simplifications of the complex task of cooking a steak. Models help us understand a complex part of our world by simplifying the complexity into something that’s palatable (pun intended). Determining which model is the best model depends on you and your goals and various constraints. Do you want a quick-and-dirty method? Then methods #1 and #2 are probably the best for you. Do you like the challenge of an involved and detailed recipe? Then play with method #3 and tweak the model until you get exactly what you’re looking for.
I’ve stretched this analogy too far already, so I’ll leave this post here for now. I’ll touch on many other aspects of climate and climate modeling in upcoming posts.




Tuesday, May 6, 2014

The New National Climate Assessment and Related Resources

Earlier today, May 6, 2014, the new National Climate Assessment (NCA) was released. There's an event being webcasted from the Whitehouse right now (until 4:00 pm EDT). That's here: http://www.whitehouse.gov/live


The NCA has a few different access points online. It's a massive document with lots of rich multimedia. For accessing the NCA itself, you might start on http://www.globalchange.gov which provides links to various pieces of the report.

NOAA has also put together a page specifically for educators: http://www.climate.gov/teaching/2014-national-climate-assessment-resources-educators. This nicely breaks out the NCA's Report Findings, and I've cut and pasted that below. 
  • Report Finding 1: Global climate is changing and this is apparent across the United States in a wide range of observations. The global warming of the past 50 years is primarily due to human activities, predominantly the burning of fossil fuels. Learn More
  • Report Finding 2: Some extreme weather and climate events have increased in recent decades, and new and stronger evidence confirms that some of these increases are related to human activities. Learn More
  • Report Finding 3: Human-induced climate change is projected to continue, and it will accelerate significantly if global emissions of heat-trapping gasses continue to increase. Learn More
  • Report Finding 4: Impacts related to climate change are already evident in many sectors and are expected to become increasingly disruptive across the nation throughout this century and beyond. Learn More
  • Report Finding 5: Climate change threatens human health and well-being in many ways, including through more extreme weather events and wildfire, decreased air quality, and diseases transmitted by insects, food and water. Learn More
  • Report Finding 6: Infrastructure is being damaged by sea level rise, heavy downpours, and extreme heat; damages are projects to increase with continued climate change. Learn More
  • Report Finding 7: Water quality and water supply reliability are jeopardized by climate change in a variety of ways that affect ecosystems and livelihoods. Learn More
  • Report Finding 8: Climate disruptions to agriculture have been increasing and are projects to become more severe over this century. Learn More
  • Report Finding 9: Climate change poses particular threats to Indigenous Peoples' health, well-being, and ways of life. Learn More
  • Report Finding 10: Ecosystems and the benefits they provide to society are being affected by climate change. The capacity of ecosystems to buffer the impacts of extreme events like fires, floods, and severe storms is being overwhelmed. Learn More
  • Report Finding 11: Ocean waters are becoming warmer and more acidic, broadly affecting ocean circulation, chemistry, ecosystems, and marine life. Learn More
  • Report Finding 12: Planning for adaptation (to address and prepare for impacts) and mitigation (to reduce future climate change, for example by cutting emissions) is becoming more widespread, but current implementation efforts are insufficient to avoid increasingly negative social, environmental, and economic consequences. Learn More

Note that the NCA has much of it's info broken down by region, so you can focus on what's most relevant where you are. 

There's also a series of related videos found here: http://vimeo.com/channels/nca. I've embedded the Health Chapter Video below.


All of this is brand new today, so I've not had much chance to explore (though I did look at earlier public drafts). If you find things especially helpful for learning and teaching about climate change, it'd be nice to share it in the comments below.

About the Author

Don Duggan-Haas is Director of Teacher Programs at the PaleontologicalResearch Institution in Ithaca. Along with colleagues Robert Ross and Warren Allmon, he authored The Science Beneath the Surface: A VeryShort Guide to the Marcellus Shale.