How do we predict the future?

This module will help students to understand:

• How we can make climate predictions

• The difficulties of making climate predictions

• What do these predictions look like?

• How will they affect different parts of the world?

How can we make a climate prediction when we cannot forecast the weather for the next month?

The chaotic nature of weather makes it unpredictable beyond a few days. To predict the weather you need to know exactly what is happening in the atmosphere down to the smallest scale. Climate is the average weather pattern of a region over many years (usually a period of 30 years).
 
Weather forecasts are very dependent on knowing exactly what is going on in the atmosphere, down to the smallest scales (it is ‘chaotic’), climate forecasts do not to the same extent.
 
Climate is the long term average of weather, including its variability. Climate predictions tell us about how the trends and patterns will change: will it be generally wetter in winter? Will there be more heavy downpours?
 
Projecting changes in climate due to changes in atmospheric composition or other factors is a much more manageable task than predicting the weather. As an analogy, while it is impossible to predict the age at which any particular man will die, we can say with high confidence what the average age of death for men is.
 
Similarly, a climate prediction might say that average summer rainfall over London is predicted to be 50% less by the 2080s; it will not predict that it will be raining in London on the morning of 23rd August 2089.

How do we make climate predictions?

The only way we can project climate for the next 100 years, is to use very complex mathematical models. Some of the biggest models contain ten million lines of computer code and require some of the world’s largest super-computers to run them.

These complex mathematical models contain equations that describe the physical processes at work in the atmosphere, ocean, cryosphere (areas of ice and snow) and on land. We use changes in greenhouse gas, solar and volcanic emissions to drive the climate prediction models.

Graphics 2.06 and 2.07 show how a computer model can reproduce past (observed) climate changes accurately. This gives us confidence for future simulations.

Scientists are confident that the models can provide useful predictions of future climate, partly because of their ability to reproduce observed features of current climate and past climate changes, such as the larger degree of warming in the Arctic and the small, short-term global cooling (and subsequent recovery) which has followed major volcanic eruptions, such as that of Mt. Pinatubo in 1991.

Why are some aspects of climate change harder to predict than others?

Believe it or not, it is much easier to predict global temperature than rainfall in Beijing, Jakarta, London or Mexico City! This is because, the smaller the scale of the physical processes involved, the harder something is to predict.
 
Climate models allow scientists to predict some aspects of climate change with much more confidence than others. For example:

• Averages over the whole Earth are easier to get right than very local changes

• Temperature is easier than rainfall, which depends on the very small scale physical processes going on in clouds

• Predicting how the climate will change in the relatively near future (within the next 40 years, say) is easier than further ahead, as we have a better understanding of what the world and the climate system will look like

Why are not all climate predictions exact?

There are many stages involved in making climate predictions. These include estimating future levels of greenhouse gas emissions or calculating the effects of those emissions on the global climate and then local climates. Each stage involves an increasing amount of uncertainty.
 
There are many stages involved in making climate forecasts. These include:

• Making estimates of the gases and particles that will be released into the atmosphere in the future. These are created by making assumptions about population growth, energy use, economic and technological developments

• Using carbon cycle models to convert emissions to concentrations of greenhouse gases in the atmosphere. More assumptions have to be made, based on our knowledge of things like how ecosystems respond to changing carbon dioxide availability etc

• Using full climate models to calculate the effects of increasing greenhouse gas concentrations on global climate. There are uncertainties in the models themselves, mainly due to the fact that very small scale processes have to be represented in a fairly coarse sort of way, as well as uncertainties in our knowledge of the climate system – are there feedback mechanisms that will come into operation that we don’t know about?

• Translating global change into local impacts, a whole range of more uncertainties come into play, like how local land use change will impact on the chances of a particular river flooding

What are "climate feedbacks"

A climate feedback happens when an initial change in the climate system triggers a process that either intensifies or reduces the initial change.

Imagine snow and ice melting, exposing the darker land or water beneath. This land or water will now absorb more of the Sun’s energy, rather than reflecting it back into space. This causes warming. If it is warmer, there is more melting, more energy absorbed and then more warming and so on. This is an example of a positive feedback.

There are many feedback mechanisms in the climate system that can either amplify (‘positive feedback’) or diminish (‘negative feedback’) changes in the Earth’s climate. Here are two more examples:

Water vapour

The water vapour feedback in terms of the direct greenhouse effect is positive. As the atmosphere warms due to rising levels of greenhouse gases, its concentration of water vapour increases. As water vapour is a greenhouse gas, this in turn causes more warming. This feed back may be strong enough to approximately double the increase in the greenhouse effect due to the added CO2 alone.

Clouds

Clouds can amplify (increase – positive feedback) or diminish (decrease – negative feedback) warming. Clouds are effective at absorbing infrared radiation emitted from the Earth, re-radiating that energy (heat) back to the ground and therefore exert a large greenhouse effect, warming the Earth. However, clouds can also reflect away incoming solar energy, cooling the Earth. A change in almost any aspect of clouds, such as their type, location, water content, cloud altitude, particle size and shape, or lifetimes, affects the degree to which clouds warm or cool the Earth. Some changes amplify warming while others diminish it. The feedback of clouds can therefore be positive or negative depending on the circumstances.

The feedback examples presented here are just a few of the feedback mechanisms which exist within the climate system.