Dr. Ricky Rood's Climate Change Blog

Models(3) Predictable Arguments:

By: RickyRood, 5:58 PM GMT on January 28, 2008

Models(3) Predictable Arguments:

In the comments to the previous blog there was this quote attributed to John Christy in a Wall Street Journal Commentary.

“It is my turn to cringe when I hear overstated-confidence from those who describe the projected evolution of global weather patterns over the next 100 years, especially when I consider how difficult it is to accurately predict that system's behavior over the next five days.” (John Christy WSJ Nov 1, 2007)

This statement implies that the ability or the inability to predict weather at five days has a direct relationship to the ability or the inability to predict that the planet will warm because of the increasing burden of greenhouse gases. There is no knowledge-based foundation to support this assertion.

Start: Consider the role of weather in the climate system. Atmospheric motion comes in response to temperature variability. It is straightforward to show that if there is temperature variability (a temperature gradient), then there will be pressure variability (a pressure gradient), and pressure gradients are the initiator of motion in the atmosphere. Because of the tilt of the Earth’s axis, the planet gets more heating in the tropics and less at poles. From the point of view of climate, weather in the atmosphere and motion in the ocean, exists to smooth out this persistently forced equator to pole temperature difference.

From that global climate perspective weather is like turbulence. If you were to place a smoky oil lamp in the corner of a closed room, it would be difficult for you to say exactly where you could smell (detect) smoke at any moment, but with some certainty you could say that the room would get smoky. We can say with absolute certainty that the atmosphere will maintain its role of moving heat from the equator to the pole. We can confidently conclude that if the temperature difference between the equator and the pole changes, there will be an impact on the weather.

Forced Behavior: Consider another example, the surface of a lake or the ocean. There are generally waves on the surface. It is difficult for you to stand on the shore and to predict the behavior of each particular wave and the how those waves will interact with each other. You could, if you identified a specific wave, predict with some skill how that wave would propagate for a while. You could predict, for instance, when it gets to shore. If you set out to predict all of the waves it would drive you crazy. That’s the weather problem, predicting all of those individual waves.

What if the wind increased? You could predict with great certainty that the waves on the surface of the ocean would get larger. You could predict how far the waves would impede on the beach. There are many attributes about the waves that you could predict that would provide useful, actionable information for someone with a house on the beach. Yet, you would still not be able to very accurately describe each of the individual waves.

In the above example the increased wind forces the wave field to a new basic state.

Weather and Climate: The weather problem is largely a problem of predicting waves that are sloshing around trying to smooth out local variations of thermal energy. We can pick out the weather systems with our observations, and make useful predictions. But we have to keep observing and picking out those systems to make useful predictions. The climate problem is one of increased forcing. If we change the atmosphere to hold more energy near the surface of the Earth, it will warm. This climate prediction is independent of the ability to predict the weather.

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In the Wall Street Journal commentary Christy speaks of being humbled by the complexity of the climate system, and then states a belief:

“Mother Nature simply operates at a level of complexity that is, at this point, beyond the mastery of mere mortals (such as scientists) and the tools available to us.” (John Christy WSJ Nov 1, 2007)

This is a belief. It is not a belief that I share.

Consider complexity.

Watch the movie Apollo 13 . The complexity of the rocket launch and a successful lunar mission is staggering. Apollo 13 was the one where there was a major failure in the space vehicle. This magnified the complexity. Rocket science is about complexity, but like the weather and climate problem, the physics of rocket are astoundingly simple and well understood. We push around objects in the field of gravity. It would have been easy to look at that accident and say that the complexity is staggering; it can’t be understood; there is no actionable information. That is not what people did.

Rockets though are the product of humans. Consider your body. It is complex. It is difficult to predict with precision what a drug will do to an individual. It is difficult to predict how an individual will respond to a particular environmental exposure. Does this inability to predict the response of a particular individual, mean that warnings about excess heat and dehydration have no value? Does this inability to understand and represent the entire complexity of even a single human, tell us that there is no sense in pursuing medical solutions?

I stand humbled by the complexity of the Earth. That does not mean that I look at the Earth and conclude that its complexity is beyond the ability of people to understand what is happening and what is going to happen.

I am reminded of a friend of mine who did not believe in getting medical procedures. He had a heart attack and survived. He knew, then, that he had heart disease. He knew he could get it treated, but he chose not to get it treated. He knew he would die earlier if he did not get it treated. It was a belief of his, and it was a belief that, in the end, was his alone to make.

There is a profound difference from my friend’s individual decision and when we have knowledge about what is going to happen, and that knowledge is consequential to all. Be clear - it is a belief that the complexity of Mother Nature is beyond mere mortals to understand – John Christy’s personal belief. There is tremendous evidence that we can approach Mother Nature’s complexity, extract information, and provide knowledge about the future. It is responsible to take action on this knowledge because the impact is not individual.


Here are the previous blogs on models.
Uncertainty and Types of Models
Models (1) Assumptions
Models (2) Forgotten Layers



Chapter 16: Fundamentals of Modeling ....


Updated: 9:41 PM GMT on November 08, 2009

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Models(2) Forgotten Layers:

By: RickyRood, 5:40 AM GMT on January 23, 2008

Models(2) Forgotten Layers:

The comments on the blog have been pretty intense lately. Be good to each other. I’ll talk about the 1930s, when I know a little bit more. Maybe we can figure out some good model experiment. I want to continue with a thread I started a couple of blogs ago; the one on models. In the previous entries I wrote about the different types of models: heuristic, statistical, and physical. I wrote about what is assumed in building a model, and those things which are known with significant certainty. In this blog I want to talk more about the different types of models and this idea that keeps coming up that there things that scientists don't include because they are not "on message"

Here are the previous blogs on models.
Uncertainty and Types of Models
Models (1) Assumptions

Layers of Models: Climate models and weather forecasting models are closely related. Originally, climate models were “atmospheric” models. Since the late 1960’s climate models have evolved to include the atmosphere, the ocean, the land and soil hydrology, and sea ice. These are the primary component models of a physical climate model. Each of these components was initially developed in their own discipline of study; that is, ocean models were developed by oceanographers to study the ocean. Using this as an example, the ocean models view the atmosphere as a boundary condition, which provides temperature, wind stress and other parameters needed to “force” the ocean circulation. When these models are put together, often called coupling, each component provides interactive information for the other components. When the model is coupled it can simulate – or not – modes of variability that require the interaction of, say, the atmosphere and ocean (like El Nino). I have introduced two ideas here that will come back in the future; they are: motions that are forced and motions that are internal variability. Both are important in the climate problem.






Figure 1: "Components" of the physical climate system. These are the major component models of a climate model.


A couple of paths to go down:

First Path: The computational challenges that confront modelers are large. Each component model can expand in complexity and resolution to consume available computing resources. Resolution is how coarse or fine the model represents the Earth. When the component models are put together, they are generally simpler components than would be used in stand-alone studies. Often the simplification is a more coarse resolution.

Once these models are put together, however, that is not the end of the story. The component models still exist, and they can be used for experiments that require more complexity and resolution to understand the observations. As component models they can have as boundary conditions (forcing) either output from other models or observations. The purpose of these experiments is to investigate the processes that determine cause and effect. This allows evaluation of how well the process is represented in the coupled model.

It is this ability to perform focused experiments at the process level that tells me the confidence level that I can have in the coupled model. The Earth supports many scales of variability, and models are multi-scale. The robustness of climate predictions is based not just on the results of coupled climate models, but on analysis of this multi-scale system and the ability – or not – of the models to represent observations. Models are far from perfect, but as I have discussed earlier, we make decisions all the time with imperfect information.

Here is a link to the Community Climate System Model. There is data here from model simulations that you can investigate on your own. (Question: If you had the ability to be running a IPCC climate model in the background on your PC would you?)

Second Path: The components described above are the physical climate system. The biology in the ocean and on the land needs to be included. Chemistry in the atmosphere and the ocean and on land needs to be included. Aerosols, particulates in the atmosphere, need to included. There are efforts on all of these. Atmospheric chemistry is especially well developed, and there are climate models with coupled chemistry. Chemistry, however, requires inclusion of many species – a source of significant expense. Aerosols are important to both chemistry and climate; they are the focus of much of the current model development.

Biological modeling is less well developed than the atmosphere, ocean, land, and sea ice. The strategies for looking at the biology require taking a “big picture” view of the biology; for instance, what is the flux of carbon dioxide or sulfur species. There are also models and measurements of the Sun and its variability.

There are assumptions in all of these model components. Some are well founded, and some are crude. In no case is “nothing known.”

The Forgotten and that not included: Occasionally in the comments the statement is made that the climate scientists have ignored some important process. I frequently sit in meetings where I am learning a new subject, and I wonder whether or not something has been considered or forgotten. For the climate problem, I have yet to find something that has been truly forgotten. Every suggestion I have seen, I have followed up, and there is a substantial literature. That does not mean that the process should not be revisited, but I can’t find the forgotten. The credibility, the stubbornness, the obsessions, and the vanity of scientists doesn’t really let that happen.

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Chapter 16: Fundamentals of Modeling ....

Updated: 9:42 PM GMT on November 08, 2009

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Water, Water, Water (1)

By: RickyRood, 2:20 AM GMT on January 15, 2008

Water, Water, Water(1):

The observed and predicted changes to the climate that are forced by carbon dioxide and other greenhouse gases is often called “global warming.” Few argue that the Earth has warmed in the past century. There is a large body of evidence that this observed warming is related to increasing greenhouse gases. Greenhouse gases increase because of the burning of fossil fuels. This warming can be distinguished from warming that occurs due to known sources of natural variability. If there is warming due to unknown sources of variability, well – we really can’t say anything about that which we know nothing about. We continue to look for that which we do not know, but the likelihood of us finding a mechanism other than greenhouse gas increases to explain the current warming is very low. The physics of the warming are simple and robust, and the information collected from many sources is consistent to a very high degree. There is some information that is inconsistent or still not satisfactorily explained. I cannot point to any inconsistency that I am aware of that would be a potential smoking gun to refute the basic tenets of “global warming.”

Warming is the most simple and the most sure prediction. An increase in sea level rise due to both the warming of the oceans and melting of ice on land is also quite certain. The change in sea level is an indicator of the role of water in the climate. In a consequential way, climate change is more about water than it is about warming. The difference between ice ages and temperate periods is a difference between water being stored as ice, liquid, or vapor. The temperature of the atmosphere strongly influences the amount of water vapor that can be held in the atmosphere as well as the amount of ice that exists on land. It is the balance between the different phases of water that defines climate regimes like the ice age, temperate periods, and a greenhouse.

People, economies, and ecosystems have evolved or adapted to the balance of water. From a basic biological point of view, people have shown the ability to survive in virtually all types of watery environments. The ability to thrive is perhaps most closely related to the ability to produce the energy of food crops, and this ability is strongly linked to having water available in the liquid phase. In today’s world there are enormous amounts of water used in the production of energy. It’s used to cool and clean and heat for stream. In the productions of bio-fuels such as ethanol, water is used at many stages in the process. Water, therefore, is at the center of it all. It is central to the stability of the physical climate, ecosystems, agriculture, energy production, and, of course, we need water to drink.

An important point is that water is a stressed resource. Water is a stressed resource, and climate change is an additional stress on this resource. Climate change is not the cause of the stresses on water. In most cases climate change will amplify the stress on water resources. There might be some places where climate change improves the availability of liquid water. Even in this case, however, changes in the expected distribution in water will challenge the large engineering projects that we use to manage water. Water is one of the places where climate change, engineering, policy, and litigation intersect most strongly.

The balance of water between vapor, liquid, and gas is not only of central interest to the global climate, but the phase transitions of water also play a central role in weather. When water changes from vapor to liquid or from liquid to ice, energy is released. When ice melts and liquid evaporates energy is absorbed. This energy is in the form of heat. In clouds there is ice, liquid, and vapor, and conversion of vapor to liquid and ice is important not only for the production of precipitation, but for the release of heat. The release of heat then makes the air more buoyant and the clouds rise higher.

The energy released by water is important to developing weather systems. As many WU users know, there are some significant differences between weather systems in the tropics and weather systems in middle and high latitudes. While the release of energy by the condensation of water vapor is important in most weather systems, this mode of energy conversion is more important in the tropics than outside of the tropics.

Water is also a greenhouse gas. In fact, in the Earth’s radiative balance, water is by far the most important greenhouse gas. Its influence on the radiative budget is larger than the influence of carbon dioxide. Carbon dioxide works on the margins; those who own a business know how things are won and lost on the margins. One way to think of carbon dioxide is like having a window cracked. The quirks of the heating system in my apartment make it very warm, and a small crack in the window makes the place habitable. The surface of the Earth cools directly to space in only a small crack in the sky. Carbon dioxide is closing that crack. (Who remembers Crack the Sky?)

The oceans of the world are huge supplies of water. As the atmosphere and the oceans warm, more water vapor is held in the atmosphere. The consequences of this are 1) there is more water to act as a greenhouse gas, which warms the surface some more (a positive feedback effect); 2) there is potentially more energy to be released in developing storms, and hence, the potential for stronger winds; 3) there is more water available for rain and snow, and hence, the potential for heavier rain and more floods; 4) the fundamental modes by which middle latitude storms get their energy might change with time. (Looking forward to the responses from this one! Plus, where do more droughts come from? Of course there is the huge complication of clouds that fascinate meteorologists, climatologists, and the committed blog readers of the world.)

Water, water, water: Just because the globe is warming does not mean that it ceases to snow. In fact, there are many situations and places where the snow might increase. That the planet is warmer does not mean that it no longer gets below freezing. The high mountains near the coast, like the Cascades and the Sierra Nevada would expect more snow. This is also true for the high altitudes parts of Greenland and Antarctica. It is reasonable to expect stronger snow storms in many parts of the eastern U.S. because 1) there is more water in the air, and 2) it still gets below freezing. Near the Great Lakes more snow is possible because the lakes don’t freeze over, and they can keep supplying moisture to the air.

The real challenge of water is how long it stays as ice and snow. If it is ice and snow and it melts slowly, then it is a good supply of water. If it melts rapidly, then it can be a flood and might be lost for summer irrigation. If there is snow on the ground it reflects solar radiation back to space. If there is snow on sea ice it keeps the ice from melting in the summer. For climate an important variable is how long the snow stays around.



Figure 1: The water cycle from the U.S. Geological Survey

Updated: 9:43 PM GMT on November 08, 2009

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Models(1) Assumptions:

By: RickyRood, 4:39 PM GMT on January 06, 2008

Models(1) Assumptions:

There have been some strong statements in the comments of late. There has been a thread about models, including some who have noticed that my career has something to do with models and just what do I think about models. It is not something that I can write about in one blog, but I will start a thread on models, and interweave some of the other ideas that have percolated through the recent discussions.

Models: A little personal background. Many would consider me a modeler; I came to modeling with a little reluctance. I held some serious doubts about the ability to represent, quantitatively, atmospheric circulations. I imagined that there was some way that I was going to divine more information out of analytic analysis of the equations that describe atmospheric motion. What I learned, there have been many smart people who have been looking at those equations, they are difficult to solve analytically the way you might imagine solving an algebra problem, and they support an incredible variety of types of motion. With some reluctance, back in the 1970s, I cast the equations that describe planetary waves into their numerical form. Planetary waves are those long waves that you see on weather maps, anchored by the mountain ranges and the geographical distribution of the continents. I was amazed at how well the numerical solution to the equations represented not only what I saw in the atmosphere, but could also represent analytic solutions derived from more simple equations. I learned that numerical models worked. I also learned that excruciating attention and rigor are required to make a good model. (Referring back to the previous blog, these are “physical” models.)

I do not exactly consider myself a modeler. My scientific career has always been driven, first, by the observations, but I have developed and used models to interpret those observations. My development efforts have focused on building models that represent the physical mechanisms that are revealed by the observations. I had the good fortune to work with Shian-Jiann Lin (now at the Geophysical Fluid Dynamics Laboratory) and to contribute to the development of a technique used in many climate and chemical-transport models.

Many of the comments to previous blogs have stated that models are “just” a set of assumptions, and that the processes in the Earth’s climate are so complex that they defy our attempts to model them with any rigor. There are assumptions that are made when models are built, but those assumptions are not simply pulled out of a bag of magic tricks. In fact, arbitrary, unjustified assumptions are generally repelled from the modeling community because, first and foremost, the models need to describe some set of observations and the evolution of those observations, i.e. prediction. Most components of models are, just like my original modeling efforts, a representation of mathematical equations that rigorously describe the motion of the atmosphere. The idea that I and my colleagues would work from some potpourri of unjustified assumptions is, in fact, a bit offensive. (The “assumptions” in climate and weather models are often based on “statistical” models ... see previous blog.)

As for complexity, yes, each and every part of the Earth system, the atmosphere, the ocean, the land, the ice is complex. When chemistry and biology is added to the mix, the complexity increases. How to represent this complexity is a challenge; we make progress; we make mistakes. We are virtually always guided by observations. Sometimes the observations are not complete; they are only a glimpse into a process. Our attempts to model them often motivate and guide the quest to take more observations. Occasionally, we learn that our model of a particular set of observations is wrong. This is progress.

There are many directions I could go here. ---

Models are solved on computers, and this is cited by some critics as an intrinsic weakness. This criticism I do not understand. We, scientists and society, have been making such calculations for decades. Every time we fly we count on a set of model calculations for the successful flight of the airplane. The success of launching satellites and having them orbit and land where they are supposed to land relies on models solved on computers. I mention these two examples because like the atmosphere they depend on fluid dynamics (wings moving through the air), the rotation of the Earth (navigation), and basic equations of motion. The physical principles of the atmosphere, the ocean, aeronautics, planetary motion are pretty much the same. Classical physics with, now, centuries of success. The phenomena they describe are complex, complex but not magic or divine. (It amazes me - the Space Shuttle not only lands on the right runway, it lands on the little stripe in the middle of the runway.)

One more idea in this first installment on models – There are many types of models. Any observer of the weather knows that we have different scales of motion. There are tornadoes (small), hurricanes (medium), and synoptic waves, like the ones battering the California and the Sierra Nevada the last few days (large). We can write a model that describes these isolated phenomena with significant accuracy. When we put together a global model these scales are not all represented with full fidelity. A global climate model will represent, intuitively, large scales well, medium scales less well, and the small scales crudely. We use the process models and process observations to analyze the impact of the deficiencies of the climate models. We use the process models and the process observations to develop better climate models.

That’s it for the first installment. Models of the climate are complex; they are imperfect. They are based on sound physical principles that describe the motion of the atmosphere and ocean. Models are always based on observations, and their performance is evaluated with observations. While there are assumptions in the building of models, these assumptions are not arbitrary and capricious. Models are an important and validated element of weather and climate science. They should not, however, be used without critical evaluation of their capabilities.

Link to a document on modeling (and more!) below the figure. It's free to you!

r




Figure 1: The chapter linked below is in this book. (Book link.) This chapter, which I wrote, is aimed at introducing non-modeling scientists to the basics of modeling. You might find it useful. (I get no money if you were to buy this book.) If you read this, well, who knows?



Chapter 16: Fundamentals of Modeling ....

Updated: 9:44 PM GMT on November 08, 2009

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About RickyRood

I'm a professor at U Michigan and lead a course on climate change problem solving. These articles often come from and contribute to the course.