Uncertainty in Climate Change Predictions ; Feedbacks

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.)

Some of the important components of the Earth's climate system.

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. 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.

Examples of Feedback Processes

Several examples of important feedbacks are provided in this section. You are expected to understand these examples by name. 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.

1. The Planck Feedback is a restatement of one of the universal radiation laws, and simply says the higher the temperature of an object, the more energy it radiates. The Planck Feedback is a very strong negative or stabalizing feedback. Consider an object in radiative equilibrium, i.e., radiation energy absorbed (input) is equal to radiation energy emitted (output). Now suppose, the radiation energy input were to get smaller. The object is no longer in radiative equilibrium since energy input is now less than energy output. In response to this forcing the object's temperature will decrease. This lowers the radiation energy output and will bring the system back into radiative equilibrium. This is a negative feedback since the response of the system is to bring it back toward where it started, which was a state of radiative equilibrium. You should be able to convince yourself that if the initial perturbation were to increase the energy input, the response would be for the temperature to increase. The system starts at radiative equilibrium and the response to a perturbation is to bring the system back into radiative equilibrium, which is 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. This is shown stepwise below.
   • The Earth is in radiative equilibrium (radiation energy absorbed from sun equals radiation energy emitted from Earth to space)
   • Humans add greenhouse gases to atmosphere (the purturbation)
   • Reduction in radiation energy emitted from Earth to Space
   • Surface temperature warms (response to radiation imbalance)
   • As surface warms, more radiation energy emitted from Earth to space. Warming continues until energy output again equals energy input
   The sequence of events above is a negative feedback system, since the response of the system was to bring it back toward where it started, which was a state of radiative equilibrium. As mentioned above, ignoring all other feedbacks, the equilibrium change in global average surface temperature of the Earth for a doubling of carbon dioxide relative to pre-industrial times (560 ppm from 280 ppm) is about 1.2°C. However, that is not a realistic assessment of the expected change in glboal average surface temperature because we know there are many other 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 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 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 feedback processes makes this question impossible to answer with certainty.
2. The snow/ice albedo feedback is a well known positive feedback. Albedo means reflectance. The feeback arises because the Earth's surface has a higher albedo (or reflectance) when it is covered with snow and ice, compared with bare ground and open ocean. Thus, when the Earth's surface has more snow cover and sea ice cover, it is more reflective of solar radiation, and absorbs less radiation energy from the Sun. Consider the sequence of events:
   • Humans add greenhouse gases to atmosphere (the purturbation)
   • This causes the surface temperature to warm
   • This results in less snow and sea ice cover
   • Since the Earth's surface has a lower albedo, more radiation energy from the Sun is absorbed
   • This causes the surface to warm further (ultimate response to perturbation)
   This is a positive feedback because the ultimate response, or last step in the process, reinforced one of the previous steps. This time the system was pushed toward getting warmer and the response was to continue to push it in the direction of getting warmer. The fact that the final response is warming is not what makes it a positive feedback. For example, suppose the first step were to make the surface temperature get colder. This would still be a positive feedback system. The response would be more ice and snow cover, less radiation absorbed by the Earth, and surface temperature getting colder. This is positive because the initial perturbation was cooling the surface and the response of the system was to intensify or reinforce the cooling of the surface.
3. The CO₂ fertilization feedback is a negative feedback related to the possibility that many plants may actually grow faster and more efficiently with higher levels of CO₂ in the atmosphere. We know from studies of plant growth that increasing atmospheric CO₂ can lead to faster plant growth in some plants. In the real world, plant growth depends on many factors beside CO₂ concentration. For plants, whose growth is limited by factors not related to CO₂ availability, this feedback may not be important.
   • Humans add CO₂ to the atmosphere
   • In response, plants grow faster, taking CO₂ out of the atmosphere at a faster rate
   • CO₂ levels in the atmosphere decrease
   This is a negative feedback system because the last step diminishes or acts against one of the previous steps. In this case humans add CO₂ to the atmosphere, but in response, plants remove some of it, or bring the system back toward where it started. Notice that if the first step were that humans found a way to remove CO₂ from the atmosphere, this would still be a negative feedback system, because the response in that case would be plants grow slower and CO₂ would begin to increase.
4. The water vapor feedback is an important feedback within climate models and controversial as well. The basic concept is that as the surface temperature of the Earth warms, the rate of evaporation increases and warmer air can potentially hold more water vapor, thus the amount of water vapor in the atmosphere will increase. Since water vapor is the most important greenhouse gas on Earth, the addition of water vapor will act as a positive feedback.
   • Humans add greenhouse gases (like CO₂) to the atmosphere
   • The surface temperature warms
   • In response, the amount of water vapor in the atmosphere increases
   • Since water vapor is a greenhouse gas, the surface temperature warms further
   In many climate models, this positive water vapor feedback is responsible for much of the predicted warming ... it is not the CO₂ directly. However, the increase in water vapor predicted by most climate models is not certain to occur. While the rate of evaporation would increase with a warmer surface, it is unclear how the rate condensation will change around the climate system. The movement and evolution of water vapor and clouds within climate models is highly parameterized and difficult to correctly simulate. In fact it is difficult to even obtain good observations of water vapor to compare with climate model predictions. In many climate models, the atmospheric relative humidity remains nearly constant, so higher temperature means more water vapor. Even though global average surface temperature has measurably risen by about 0.8°C over the last 100 years, it has been difficult to observe and understand changes in water vapor that have taken place over that time period. Thus, the predicted model feedbacks related to responses in water vapor remain uncertain.
5. The cloud albedo feedback can be a positive or negative feedback depending on whether cloud cover would increase or decease as the surface temperature increases. In this section, we assume that cloud cover would increase as surface temperature increases.
   • Humans add greenhouse gases to the atmosphere
   • The surface temperature warms
   • In response, cloud cover increases
   • Clouds reflect radiation from the sun, thus the Earth reflects more radiation energy from Sun and absorbs less
   • Less radiation energy absorbed from the Sun means the surface temperature gets cooler
   The sequence shown above is a negative feedback, since the change in cloud cover acted against the previous step of warming the surface temperature.
6. The cloud emission feedback can also be positive or negative depending on whether cloud cover would increase or decease as the surface temperature increases. In this section, we assume that cloud cover would increase as surface temperature increases.
   • Humans add greenhouse gases to the atmosphere
   • The surface temperature warms
   • In response, cloud cover increases
   • Cloud bottoms emit infrared radiation toward the surface of the Earth, so more clouds, more radiation energy absorbed by ground
   • More radition energy absorbed by surface, means surface warms further
   The sequence shown above is a positive feedback. Thus, even if increasing the global surface temperature causes an increase in cloud cover, the overall cloud feedback may be positive or negative depending on whether the albedo feedback or the emission feedback is stronger. This would depend on the type of cloud that increased, the altitude of the clouds that increased, and other factors. Which one would win out? In today's climate, based on satellite obervations it appears that clouds have more of a cooling effect than a warming effect on global average surface temperature. But this could easily change in the future under a climate change senario. In any case, 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.

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. 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

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.