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

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, 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:

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 which provides links to various pieces of the report.

NOAA has also put together a page specifically for 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: 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.

Tuesday, March 18, 2014

The 2014 Draft New York State Energy Plan as a Gateway Drug to Energy Literacy (Part 3)

This post has been revised to reflect that the deadline for comments on the 2014 Draft New York State Energy Plan has been extended to May 30, 2014.

This post is the third in a series that addresses the 2014 Draft New York State Energy Plan and some public reactions to that plan. As mentioned in the last post, this commentary relates to New York State and its energy planning most directly, but the nature of what is happening here related to energy is instructive for both other areas of the country and for other controversial topics.

It is recommended that you start with Part 1. And, here's Part 2, if you need a refresher.

The laser-like focus on stopping fracking in New York State is admirable. I strongly believe that the activist community, both in New York and around the country, have led to stronger regulations and improved safety and environmental practices. But I have concerns about unintended consequences. Advocating against one particular thing often makes you de facto for something else. If you've not thought carefully about what that something else is, success in advocacy may not bring positive change. Germany is, unfortunately, providing a strong example of this. The decision to phase out nuclear power has led Germany to burn more strip-mined brown coal in 2013 than they have in decades. See a recent New York Times story on that here.

Finding a cheap and easy source of energy, using up the easy to get stores of fuel and pursuing the remaining reserves through increasingly environmentally damaging and expensive means has happened again and again throughout our history as this cartoon from 1861 shows:
The caption reads: Grand Ball given by the Whales in honor of the discovery of the Oil Wells in Pennsylvania. 

While attention is given to transitioning to renewable sources, planning for effective transitions requires knowing where you’re starting. I’d like to help people better understand the system they are trying to change to reduce the likelihood of harmful unintended consequences.

While many speakers at the public hearing on the Energy Plan expressed the need to transition to renewables immediately, the laws of physics make that impossible (unless immediately means many years). Many cited the study led by Stanford University’s Mark Jacobson which acknowledges this reality, though some of the speakers made it sound as if 100% renewable energy could somehow happen tomorrow. Energy production and use requires a lot of infrastructure - we have one kind and need another. We can't make that new infrastructure instantly and we can't do it without using the existing energy system. Making solar panels and windmills requires energy, and to replace our current energy infrastructure, it would take lots of energy - more than can be provided from renewable sources right now.

Replacing the current energy system with one that is 100% renewable would also require lots of space. For example, if we wanted to keep the University of Buffalo's 750 kilowatt quarter-of-a-mile-long Solar Strand the same width and extend it so that it was long enough to match the generating capacity of Ontario's 6.3 gigawatt Bruce Nuclear Generating Station, we'd need to extend the Solar Strand from Buffalo to Phoenix, Arizona! I think it makes more sense to put solar on rooftops than to use bare ground, so think about how many rooftops that would require, and think about how much energy would be required to make all of those solar panels! That's for a single (admittedly very large) power plant!

None of the above is intended to imply that the draft can't be substantially improved - it can. But the gist of the initiatives are about transitioning away from fossil fuels and to reduce energy demand, and it seems to me that most of the commentators failed to address that at all.

Some very brief feedback on the Plan itself:

The Plan does need to include a brief summary of its goals and initiatives in the first few pages, perhaps in the form of an executive summary. That should be followed by a brief overview of where we're starting from - that is; it needs to educate readers about our current energy system, and important changes in that system in recent years.

All of the problems discussed above can be addressed in a range of ways, but there's one strategy that addresses them all - use less energy. And this idea is, thankfully, addressed directly and frequently in the Draft 2014 New York State Energy Plan. Unfortunately, this was only mentioned by a minority of the speakers at the February meeting.

Of course, the Plan itself warrants much more feedback than I've provided in these closing brief paragraphs. I have spent much more time discussing the perception of the Plan than I have discussing the plan itself, because perception really matters. I hope readers of this series of posts will comment on the Draft New York Energy Plan before May 30, 2014. 

I further hope that they will take a careful look at the Plan before they do so. That doesn't require reading all of its many hundreds of pages, but it does require looking closely at the content you know and care the most about. Take advantage of the electronic presentation and search for terms that you think are most important to address, and read those sections carefully. Scan through to get a feeling for completeness, and for balance. Take notes as you go and then craft it into feedback that addresses both what you think is appropriately addressed in the Plan and, being as specific as possible, address its shortcomings and offer specific suggestions on how to improve it. 

Our energy system matters a great deal for almost everything we do. I'm delighted by the interest that changes to that system brings to it. Hydrofracking is catalyzing learning and teaching about the energy system, and political action too. Let's work to make all of our roles in that as beneficial as possible. 

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.