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 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.)

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.

For instance, if the amount of carbon dioxide in the atmosphere were double what is was prior to 1750, i.e., 560 ppm, with no 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. If some positive feedback mechanism occurs 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 some negative feedback mechanism occurs 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 will 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.

The Prediction of Weather and Climate

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.

Types of interactions used in a weather forecast Model

These models are:

• Very complex
• Deal with huge quantity of data
• Require a very large number of calculations

Therefore, these models need fast computers with large memory systems.

A 10-day weather prediction takes roughly 4 hours to complete while a 100-year climate simulation can take 2 months to run.

First, the individual elements that make up the model must be specified along with measurable quantities that 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.

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.

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.

Climate models are used to study:

• changes in chemical composition of the atmosphere: doubling CO₂, sulfate aerosols;
• changes in the orbital parameters: simulating past climates;
• study the variability of present climate: very long simulations to study the occurrence of El Ninos for example;
• changes in land use: deforestation or desertification;
• climate on other planets.

Climate forecast models make calculations at widely separated points:

• Weather forecast models: horizontal grid spacing is about 30 miles; it takes about 4 hours real time to complete a 10 day weather forecast
• Climate models: horizontal grid spacing is about 150 miles; it takes about 2 months real time to complete a 100 year climate simulation