As we have seen, the concentrations of greenhouse gases in the atmosphere are increasing due to the activities of man. This increase in greenhouse gases is a "forcing" or perturbation to the Earth's energy budget and hence climate. Taken by itself, we would expect that an increase in greenhouse gases should act to warm the Earth's surface by strengthening the greenhouse effect. But exactly how much warming will take place (and other possible related climate changes) due to human emissions of greenhouse gases is uncertain since the climate system is so complex and difficult to fully understand. Currently, we are unable to determine the most important reason for recent warming and other climate changes. Are recent changes dominated by natural climate fluctuations not related to greenhouse gas increases or are recent changes mostly a response to recent greenhouse gas increases with a smaller impact from natural fluctuations? In spite of the uncertainty, the climate response to the perturbation of higher concentrations of greenhouse gases is the question that we need to try to answer.
One of the tools we use to answer the question are computer models. Computer models are the only possible way to take into account the complex interactions and feedbacks that take place within the climate system. Some of the interacting components of the climate system include:
|Some of the important components of the Earth's climate system.|
Before discussing climate prediction models, we will define feedbacks and go over some examples that should help you appreciate how feedbacks complicate the prediction of future climate. A feedback is a mechanism whereby a perturbation (or a push away from equilibrium) in one process causes a response in another process that can either intensify or diminish the initial perturbation. In positive feedback the initial perturbation is enhanced or grows due to the resulting feedback and in negative feedback the initial perturbation is dimished or limited due to the resulting feedback. A non-climate example of positive feedback is what happens when you speak into a microphone when standing next to a speaker. Your voice gets amplified and comes out of the speaker, which then gets fed back into the microphone, amplified, comes out of the speaker, and so on, growing louder and louder. In this case, the initial perturbation, speaking softly into the microphone, sets off a process that amplifies or enhances the initial perturbation, which grows into a loud sound. A non-climate example of a negative feedback process is the operation of a thermostat with a heating and cooling system. When the temperature gets too warm, the thermostat signals the air conditioning to come on. This pushes the system back against the perturbation of getting warmer. When the temperature gets too cold, the thermostat signals the heater to come on. Again this diminishes the initial perturbation of getting colder. The thermostat system stabalizes the temperature by acting as a negative feedback process, which always acts to keep the temperature near its set point. In contrast, the microphone-amplifier-speaker is a positive feedback system, amplifying the initial sound, i.e., the initial perturbation grows.
Now assume that the amount of carbon dioxide in the atmosphere were to become double what is was prior to 1750, i.e., 560 ppm. Ignoring all feedbacks (except for the negative Planck feedback, which is discussed below), we can use radiation models to compute an increase in surface temperature of about 1°C (1.8°F) in response to the doubling of CO2. We have simple radiation models which make this type of calculation. However, the simple models do not consider other feedbacks, such as might be caused by changes in cloud cover and water vapor, and therefore this prediction is likely wrong. In this case, "no feedbacks" means that nothing else in the climate system is allowed to change except the changes in radiation (and resulting temperature changes) that result as a direct consequence of higher CO2 in the atmosphere and the fundamental law of radiation emission that warmer objects emit more radiation than cooler objects (known as Planck's Law). There are no changes in cloud cover or weather patterns or anything else, which of course is unrealistic. However, it is important to keep this "non-feedback" calculation of a 1°C increase in global average surface temperature in mind when considering more detailed caculations that include feedbacks. If positive feedback mechanisms dominate in the climate system, the surface temperature will increase by more than 1°C for the same additional carbon dioxide, for example, maybe 4°C. On the other hand, if negative feedback mechanisms dominate in the climate system, the surface temperature will increase by less than 1°C, maybe only 0.5°C. An ongoing question is how sensitive is the Earth's average surface temperature to changes in greenhouse gas concentrations resulting from human activity? Therefore, getting all the feedbacks correct is extemely important in being able to accurately predict future climate changes caused by increased greenhouse gases. This is extremely difficult given that we do not fully understand the complexities of the climate system, and considering that there are limitations on computer memory and speed.
Several examples of important feedbacks are provided in this section. You will not be expected to know or understand the details of the first, known as the Planck Feedback. In fact the Planck Feedback is always accounted for even when a simulation is called a "no feedback" simulation of temperature change. You could even skip the section on the Planck Feedback if you find it difficult to follow. But, you are expected to understand the other examples (numbers 2 - 6) by name as they are much easier to understand. In addition, if you are provided with a description of a simple feedback mechanism that is not provided in this section, you should be able to identify the system as a positive or a negative feedback.
Now let's try to apply this to the issue of human added greenhouse gases. Adding greenhouse gases to the atmosphere will initially slow down the rate at which the surface cools and reduce the emission of radiation from the Earth to outer space. This means the Earth will no longer be in radiative equilibrium since energy input from the Sun is now greater than energy output. In response to this, the Earth's surface will warm up. This will increase the emission of radiation energy from the Earth to outer space. The surface will stop warming when the energy output is again equal to the energy input. The net impact of this feedback is to limit the temperature change. This is shown stepwise below.
However, the "non feedback case" is not a realistic assessment of the expected change in global average surface temperature because we know there are many possible feedbacks operating in the real climate system. Unfortunately, because the climate system is so complex and poorly understood, we are not able to accurately compute the change in global average temperature after doubling carbon dioxide. According to most current climate models, after accounting for the most important internal feedbacks, the global average surface temperature of the Earth would increase somewhere between 1.5°C and 4.5°C after a doubling of carbon dioxide. The net effect of all other feedback mechanisms within most current climate models are positive. This result of climate models has sparked much debate among climate scientists. While some believe the models are the best prediction tools we have, others argue that the response of current climate models are too sensitive to changes in greenhouse gas concentrations and the actual change in global average temperature for a doubling of CO2 will be less than 1.5°C. This is one of the big question of climate change ... how sensitive is the Earth's surface temperature to anthropogenic increases in greenhouse gases? Our lack of understanding of the net impact of internal feedback processes makes this question impossible to answer with certainty.
An important consideration is that the simulation of water vapor and clouds are two areas where climate models are known to have difficulties, yet feedbacks related to these processes are very important in the overall net positive feedback simulated by the model. This causes some people, including some scientists, to disregard the predictions of current climate models.
A nice set of short videos about cloud feedbacks has been prepared by the National Science Foundation: Clouds: The Wild Card of Climate Change.
There is no doubt that feedbacks related to water vapor and clouds are not well understood and difficult to accurately model. However, strong positive feedbacks related to changes in water vapor and clouds are predicted by most climate models. And much of the warming predicted by climate models results from these positive feedbacks, not directly from human greenhouse gas emissions. The model predictions have been accepted by the International Panel on Climate Change (IPCC). In the 2013 Summary for Policymakers, the following statements are made: (1)"The net feedback from changes in water vapor ... is extremely likely positive and therefore amplifies changes in climate." and (2)"The net radiative feedback due to [changes in] all cloud types combined is likely positive." In IPCC language, extremely likely means 95% sure and likely means greater than 66% certain. Postive feedbacks related to changes in water vapor and clouds are responsible for much of the predicted warming by climate models after humans add greenhouse gases to the atmosphere. The problem is that these process are some of the least understood process in the climate system and thus the current model predictions should be considered uncertain. Instructor's note. I believe the IPCC places too much confidence in the ability of climate models to accurately predict future climate changes and fails to properly convey the uncertainties in those predictions. This does not mean that model predictions are wrong. They may be correct. The issue is that the public is not made aware of the uncertainty in the prediction. The model prediction that the global average surface temperature is relatively sensitive to increases in greenhouse gases through strong positive feedbacks related to water vapor and cloud changes is often presented as fact without any mention of the large uncertainty associated with current model projections.
You can just read over this section to get the main points. It is not important that you study and understand the details presented. You should consider the question posed in the text below: "How can we trust a model of the atmosphere to predict the climate as much as 100 years into the future if we do not trust similar models to predict the weather 10 days in advance?"
Numerical models of weather and climate are based on the fundamental mathematical equations which describe the physics and dynamics of the movements and processes taking place in the atmosphere, the ocean, the ice and the land. Please keep in mind that these models are not reality. There is much that we do not understand about weather and climate, such as the complex feedbacks discussed above. You should realize that if we do not fully understand something, there is no way we can precisely simulate it with a computer program. In addition, there are processes that happen over time and space scales that are too short or small to resolve with the models and must be approximated, such as the formation clouds. The results of such models should be used as one tool in studying climate change and should not be interpreted as an exact prediction about how climates will change in the future. The figure below shows some of the complicated processes and interactions that must be simulated by climate models.
|Processes and interactions important in models of climate.|
These models are: very complex, deal with huge quantity of data, and require a very large number of calculations. Therefore, these climate models require fast computers with large memory systems.
A 10-day weather prediction can be completed within a couple of hours, while while a 100-year climate simulation can a month or more to run. First, the individual elements that make up the model must be specified to define the state of each element. The state of each element, or block, in our model is specified for a given instant of time by a series of numbers that define its temperature, pressure, density, humidity, wind direction and speed, and so on.
We begin the operation of our model by specifying all these numbers for every block in the model. This is the initial condition of the model and defines the state of the model at the starting time.
|Sample of the equations that control the behavior of the atmosphere|
Once these calculations are completed, we have a slightly changed model from the initial condition. Each block has updated values defining its temperature, pressure, density, humidity, wind direction and speed, and so on.
We can then repeat the process, calculating a new set of changes based on the new state of the model. What we end with is a numerical model that evolves with time, hopefully mirroring changes that take place in the actual atmosphere.
A schematic diagram of a General Circulation Model (CGM) is
shown below the sample equations. Note that
the grid cells can be of very different dimensions for different types
of models. For long range climate model simulations, a typical grid cell
is over 100 miles on a side, while for global weather forecast model simulations,
a typical grid cell is around 30 miles on a side. For smaller regional scale model
forecasts, like the
Arizonal Regional Model run in the department of Atmospheric Sciences, the grid
cells are about 1.8 miles on a side. It simply takes too much computing to run the global
models at a high spatial resolution, like 1.8 miles. One effect is that global models are
not able to specifically resolve features smaller than a grid cell, such as individual
thunderstorms and the formation of clouds, which can be resolved in some regional models.
|State of the|
atmosphere at time t
temperature, winds, etc.
|equations that describe |
the behavior of the
|State of the|
atmosphere at time t + dt
temperature, winds, etc.
|equations that describe |
the behavior of the
|State of the|
atmosphere at time t + 2 * dt
temperature, winds, etc.
An important consideration is How can we trust a model of the atmosphere to predict the climate as much as 100 years into the future if we do not trust similar models to predict the weather 10 days in advance? (see Numercal Weather Forecast Page)
Climate models are used to study: