Complexity of the Climate System Makes Predictions of Climate Change Difficult and Uncertain

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. What will actually happen to the climate of Earth is uncertain since the climate system is so complex. But 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:

• Atmosphere
• Hydrosphere (oceans, fresh water)
• Biosphere (life)
• Land surface
• Cryosphere (frozen water ... glaciers, sea ice, snow, etc.)

Feedback Processes in the 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 and in negative feedback the initial perturbation is dimished or weakened. 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. In this case, the initial perturbation, speaking into the microphone, sets off a process that amplifies or enhances the initial perturbation. A non-climate example of a negative feedback process is the operation of a thermostat. 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, while 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, we compute an increase in surface temperature of about 1°C (1.8°F). We have simple models which make this type of calculation. However, the simple models do not consider feedbacks 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 that result as a direct consequence of higher CO₂ in the atmosphere. 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 occur 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 occur in the climate system, the surface temperature will increase by less than 1°C, maybe only 0.5°C. It is even possible for there to be no change or a decrease in temperature. 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 climate feedback processes are presented in this WORD document on climate feedbacks. Much of what I expect you to learn and understand from this reading page is contained in the WORD document, so please take the time to study it! Pay particular attention to the uncertainty regarding feedbacks related to changes in water vapor and clouds.

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 not accurately modeled. However, positive feedbacks related to changes in water vapor and clouds are predicted by most climate models. This has also 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. 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.

The Prediction of Weather and Climate

You can just read over this section to get the main points. It is not important that you study and understand the details presented.

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

From here on, the model runs itself. The mathematical and physical laws governing the interactions between elements are run forward in time. In essense, we calculate how the temperature, pressure, etc., of each block changes due to all important physical processes, including the influence of neighboring blocks.

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 atmosphere

State of the atmosphere at time t + dt temperature, winds, etc.

equations that describe the behavior of the atmosphere

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)

• Weather Forecast needs to be very accurate to be useful. People want to know if it will rain or not 2 days from now.
• Climate Modeling is not concerned with a single event but whether it can generate a realistic ensemble of individual events. In other words, does the model reproduce in a statistical sense the climate for a region (e.g., average conditions, standard deviations from the average, frequency of extreme events, etc.)? For example, it is not important exactly where or on what day it rains, as long as the rainfall is statistically correct over each area of the globe for a 30 year period. Thus, it is possible for a climate model to simulate climate in a statistical sense, without correctly predicting the weather conditions on any given day.

Climate models are used to study:

• the effects of changes in chemical composition of the atmosphere: doubling CO₂, sulfate aerosols;
• the effects changes in the orbital parameters: simulating past climates;
• the variability of present climate: for example, simulations to study the occurrences of El Nino / La Nina;
• the effects of changes in land use: deforestation or desertification;
• the climates on other planets.