Predictions of Climate Change are Difficult to Make 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.)
All components of the climate system are linked, such that a change in one affects all of the others.
For example, adding greenhouse gases to the atmosphere, changes the composition of the atmosphere.
This affects the heating and cooling of the ground. This will affect rising air currents and the
formation of clouds. Changes in clouds also affect the heating and cooling of the ground. On longer timescales,
changes in cloud cover and temperature will affect the type of life that lives in a region. This
may change the land cover to a different vegetation type, which in turn changes the amount of
radiation the surface absorbs from the sun. The point is that you cannot accurately predict how the global average
surface temperature will change after increasing greenhouse gas levels without understanding all
of the linked changes to the other components of the 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. 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.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
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. 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 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.
![](ucar_model_input.jpg) |
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.