Climate Models' Predictions of Climate Change

Introdutory Notes on Climate Model Forecasts

Climate models are complex computer models that are used to simulate Earth's climate and climate changes, including changes due to increased greenhouse gas concentrations. You should always keep in mind that these models are not reality. There is much that we do not understand about the operation of the climate system on Earth, for example, some of the complex feedbacks mentioned on the previous page. Thus, the models are not perfect and do not make exact predictions. Simulations made by these models are one tool we have to help understand climate and climate change on Earth. Given the uncertainty in climate model predictions, we should also consider other sources of information when deciding environmental policy, such as restrictions on emissions of fossil fuels. In other words, the results of climate model simulations should not be the sole reason for enacting environmental policy.

Several tests have been run with climate models that show they do have some predictive skill. For example, climate models have been able to closely reproduce natural, short-term fluctuations in climate due to historic events like volcanic erruptions and El Nino / La Nina. On longer timescales, some features of the Pleistocene Ice Age cycles have been reproduced in simulations. People have been able to use climate models to closely reproduce the measured change in global average temperature that occurred during the 1900s. In these simulations, greenhouse gas concentrations are started at year 1900 levels, then the levels are increased as they were measured to increase as the model runs for 100 years (simulating the 1900 to 2000 increase in greenhouse gases in the atmosphere). The change in global average temperature for these simulations (increase of about 1°C) over 100 years is close to the measured change in global average temperature over the period from 1900 to 2000. When these same models are run without increasing greenhouse gas concentrations for 100 years, they do not show warming. Thus, according to climate models, most of the measured increase in global average temperature since 1900 was caused by increasing greenhouse gases.

However, the model's representation of Earth's climate is not reality. There no point during a simulation where the model's representation of the climate matches a known measured state of the climate. If you do try to initialize the model to some point in history, it will revert back to the model's perception of climate. Thus, while climate modelers were able to reproduce the overall change in global average temperature after adding greenhouse gases within the model world, it requires a big leap of faith to believe the same overall change would happen in the real climate system simply due to the addition of greenhouse gases. One problem with climate model simlations is that they generally fail to reproduce the known temporal (time) and spatial (position) variability that is known to happen in the real world, especially over small scales. This "natural variablity" is observed in nature but not so much in climate models. Also, keep in mind that predictions of future climate change are much more difficult than simulating past climate changes where the answers are basically known beforehand. This allows modelers to tweak their models until they get the "known" answer. Of course when making predictions of future climate changes, one does not know the answer beforehand.

Climate models continue to evolve and improve due to increases in computing power and improved observations and understanding of the climate system. However, the predictions of current climate models should be viewed with scientific skepticism.

Predictions of Equilibrium Temperature after doubling CO₂

Keep in mind that there are many different climate models and that different models make different predictions about the future. The reason is that no model can fully represent all the complex process and feedbacks involved in the Earth system. Due to this uncertainty, we should consider all of them as possible outcomes of adding greenhouse gases. There is also a realistic possibility that all models will turn out to be wrong. Perhaps the climate changes due to adding greenhouse gases will be less severe than predicted by models or perhaps we will be surprised and climate changes will be more severe than predicted by current models.

The ability of global climate models to reproduce the observed surface temperature trends over the 20th century represents an important test of the models. While most climate models are able to reproduce the slight warming in global average surface temperature that has been measured since 1860, no model is able to correctly get the spatial patterns of temperature changes correct. In other words, the observed changes in climates at the scale of regional climate zones has not been reproduced by any climate model.

As reported in the 2007 IPCC Report, various modeling studies have suggested that a doubling of atmospheric carbon dioxide from it pre-industrial value of 280 ppm, or its equivalent by incorporating the effects of increases in other greenhouse gases, will increase mean global temperatures between 2.0 and 4.5°C, with a best estimate of about 3.0°C. This means that climate models used to produce the 2007 IPCC Report all have positive feedbacks with respect to changes in carbon dioxide, since the best estimate without feedbacks is that global average temperature would go up 1.0°C after doubling CO₂. The IPCC report says that warming within this range is likely, which means probability >66%. Instuctor's note. In my opinion, the IPCC places too much confidence in climate model simulations.

You must understand that these studies were done by doubling carbon dioxide, then letting the model run until a new equilibrium climate state was reached. In reality, the increases in greenhouse gases happen over an extended period of time and the climate system takes some time to come into equilibrium.

A big issue here is that the warming in surface temperatures tends to lag behind the increase in greenhouse gases. To a large degree, this lag is due to the large thermal inertia of the oceans -- in other words it takes a lot of energy to raise the temperature of the ocean water.

Let me try to break it down into understandable steps.

• Increasing the amount of greenhouse gases in the atmosphere, intially slows down the rate at which the Earth emits infrared radiation to space.
• Assuming no change in energy input (Energy the Earth absorbs from the Sun), the Earth is no longer in energy balance since energy in exceeds energy out.
• As long as energy in exceeds energy out, the planet warms. In particular the Earth's surface temperature will warm.
• As the Earth's surface temperature warms, the amount of radiation energy it emits increases. Since some of this extra radiation energy is lost to space, the amount of radiation energy that the planet emists to space increases.
• The warming continues until the energy out increases to the point where it equals the energy input.
• The Earth's surface is 70% covered by Oceans. Oceans have a high thermal inertia, so the Ocean surface temperature is slow to warm up in response to the energy imbalance caused by adding greenhouse gases to the atmosphere. The two main reasons for this is that (1) water has a high specific heat (meaning you have to add a lot of energy to liquid water to change its temperature in comparison to many other materials), and (2) the surface temperature of the ocean is slow to warm up because the additional heat must mix throughout the entire depth of the oceans.
• Therefore the amount of warming of the Earth's surface (temperature increase) required to bring the system back into balance (energy input equal energy output) will take quite some time.

As a result of the delay induced by the oceans, some climate scientists do not expect the Earth to warm by the full 2.0-4.5°C (3.6-8.1°F) by 2060, even though the level of CO₂ is expected to have doubled by that time. Assuming this delay is real, we can make several conclusions here.

1. This delay provides an opportunity to reduce the magnitude of anthropogenic climate change before it is fully realized, if appropriate action could somehow be taken to reduce greenhouse gas concentrations.
2. Even if we were to stabalize greenhouse gas concentrations today, this delay means that the surface temperature will continue to warm until a new equilibrium is reached (this may take hundreds of years).
3. If we wait to act, still greater climate change will be expected, which will be more difficult or impossible to avoid.
4. The ocean surface temperature would also be slow to cool. Suppose in the future climate change gets out of control and we decide that we need to lower the average surface temperature of the Earth by somehow removing greenhouse gases from the atmosphere. The cooling would be very slow and lag behind the decrease in greenhouse gases. Thus anthropogenic global warming, if it takes place, cannot be quickly reversed.

Predictions of Temperature Changes for the 21st Century

The 2007 IPCC Report projects a warming of about 0.2°C per decade over the next two decades with continuing increases in greenhouse gas emissions. The report also states that even if concentratrations of all greenhouse gases had been kept constant at year 2000 levels, a further warming of about 0.1°C would still be expected (ocean delay). Some confidence in near-term projections can be gathered from the fact that the first IPCC report in 1990 projected a warming of global average temperature of between 0.15 and 0.30°C per decade from 1990 to 2005, which can now be compared to the observed value of 0.20°C per decade. However, the most recent warming trend seems to have ended sometime around 2002 and this was not predicted by any of the IPCC climate models as shown in the figure below taken from The Skeptic's Case by David Evans.

Currently, the Intergovernmental Panel on Climate Change (IPCC) projects a warming of 1.1-6.4°C (2.0-11.5°F) in global average temperature by the year 2100. This estimate is based on the latest runs of what are considered to be the best global climate models AND the most accepted estimates of future emissions of greenhouse gases. Keep in mind that we are never certain of future emissions of greenhouse gases, so various emission senarios are run by modeling groups. Part of the reason for the large spread in predictions of future warming is due to uncertainty in future emissions of greenhouse gases (refer to table SPM-3 [page 13] and figure SPM-5 [page 14] in the Climate Change 2007: Summary for Policymakers). The projected range of warming for each emission senario is considered likely by the IPCC report.

The 2007 Report also claims that there is now higher confidence in projected patterns of warming and other regional-scale features, including changes in wind patterns, precipitation, and some aspects of extremes. In previous semesters, I would point out that while all climate models predict warming of the global average temperature (giving us relatively high confidence in that prediction), individual models were all over the place when you looked at regional (small spatial scale) changes in temperature and precipitation patterns (giving us low confidence in the ability of climate models to project regional climate changes). Since I do not have time to evaluate the claim that regional projections from climate models are more confident, I will have to defer to the latest IPCC report (see pages 15 and 16 in the Climate Change 2007: Summary for Policymakers). In summary, IPCC 2007 projects that warming in the 21st century will continue to show geographical patterns of warming similar to those observed over the past several decades, i.e., warming is expected to be greatest over land and at most high northern hemisphere latitudes, and least over the southern hemisphere oceans. In spite of what the IPCC 2007 report claims, climate models have major problems in reproducing the multi-decadal climate variability that is known (through observations) to take place at regional (ecosystem-level) scales. Since global scale changes can be considered as a summation and interaction of regional climate changes, many question whether current climate models are even capable of accurately predicting future climate changes until they can better simulate natural variability.

Climate Model Predictions for Extreme Weather Events

Recall when we started this section on global warming and climate change, it was pointed out that the frequency and intensity of extreme weather events is probably more influential on the types of plants and animals that can survive in a given ecosystem than the average conditions. Therefore, any changes in the distribution of extreme events is an extremely important thing to monitor and predict.

Predicting the distribution of extreme events is a very difficult problem for climate models to answer. For one, extremes are by definition rare, which makes statistical conclusions far more difficult to draw. Another reason is that the wildest weather is often confined to areas that are smaller than global climate models can predict (typical horizontal resolution of climate models is about 150 km). In a sense this is just a re-statement of the problem mentioned above: climate model projections are more uncertain over small regional scales.

Because predicting and monitoring changes in the distribution of extreme weather events is so important to our understanding of the effects of global warming and climate change, research groups working with climate models are beginning to look at this issue. The following 10 indicies for extreme weather have been identified as target issues for climate prediction models (where available, I have added information available from the IPCC 2007 report):

• Total number of frost days
• Intra-annual extreme temperature range -- difference between the highest and lowest temperatures over a calendar year
• Growing season length -- begins after daily average temperature is above 41°F for 5 consecutive days; ends at start of 5 consecutive days when daily averge is below 41°F
• Heat wave duration index -- longest stretch of days with high temperatures at least 9°F warmer than the 1961-1990 average high temperature IPCC 2007 claims it is very likely (>90% Confidence) that there will be an increase in the number and severity of heat waves worldwide.
• Warmth of daily lows -- Percentage of time daily low temperatures fall above the 90th percentile of daily minimum temperature for 1961-1990 average
• Wet days -- number of days with at least 0.40 inches of precipitation
• Dry days -- maximum number of consecutive days with no precipitation
• Wet stretches -- maximum 5-day precipitation total
• Average daily precipitation intensity -- Annual precipitation divided by the number of days in which precipitation fell
• Influence of very wet days on annual total -- Percentage of annual total precipitation occurring on days that exceed the 95th percentile from 1961-1990 average. IPCC 2007 predicts a very likely increase in heavy precipitation events.

Currently, we have less confidence in the ability of climate models to accurately predict the above indices as compared with predicting changes in global average temperature. Hopefully, with lots of hard work, scientists can improve climate models to better answer the important question: how will the distribution and intensity of extreme weather events change in response to human activities?