Climate Models' Predictions of Global Temperature 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 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. We certainly cannot program models to be more accurate than our understanding of the underlying processes. 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 greenhouse gases. 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 reasonably 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, and the increase would not have happened without human emissions of 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 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. In other words, the models seem to underestimate natural changes in climate that happen without changes in greenhouse gases, and may overestimate changes in climate that result from changes in greenhouse gases. It is quite possible that the observed increase in temperature over the last 100 years has been wrongly attributed to the increase in greenhouse gases by climate models. Perhaps the observed change in global average temperature was part of a natural climate change and not dominated by the increase in greenhouse gases.

In order to make this point, a few lines written by Dr. Kevin Trenberth a senior climate change researcher working for the National Center for Atmospheric Research, which was orginally posted on a blog from Nature Climate Change will be discussed. The following statements were quoted from the blog, "In particular, the state of the oceans, sea ice, and soil moisture has no relationship to the observed state at any recent time in any of the IPCC models" and "None of the models used by IPCC are initialized to the observed state and none of the climate states in the models correspond even remotely to the current observed climate" both indicate that a climate model is not the real world. Therefore, starting a model in the year 1900 does not mean that the climate model looks anything like the year 1900. It just means starting the model with year 1900 greenhouse gas concentrations. It is misleading to believe that climate models have been able to reproduce the climate that happened from 1900 to 2000. The claim is that the change in global average temperature simulated by the models in response to adding greenhouse gases is the same as the change in global average temperature in the real world. Quoting from the blog, "The current projection method works to the extent it does because it utilizes differences from one time to another and the main model bias and systematic errors are thereby subtracted out. This assumes linearity." There are plenty of scientists who disagree with this last statement that the "errors" will necessarily subtract out. There are certainly important processes within the operation of the climate system that are themselves highly non-linear. Finally, considering the uncertainty in current model predictions, Trenberth said, "We will adapt to climate change. The question is whether it will be planned or not?" This indicates to me that unless we are able to improve the predicitons of current models, we are just going to have to adapt to climate changes without knowing what is coming. Instructor's Note: I include the quotes from Dr. Trenberth, who is an expert in climate modeling, because I have been criticized in the past for making similar comments about climate models. The quotes were not meant as an attack on Dr. Trenberth, who is a highly competent scientist. These were included to make a point that is not often made clear to the public.

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. In this case the change in global average temperature that took place from 1900 to 2000 was known, and models could be tested and developed to get this answer. There is a question of whether or not the models got the "known" answer for the right reasons. Some argue that much of the warming over the last 100 years was due to natural reasons not related to greenhouse gas emissions and that the models do not properly simulate natural climate cycles. Therefore, in order for the models to reproduce the observed warming from 1900 to 2000, they were programmed to be overly sensitive to human emissions of greenhouse gases. The more sensitive the models to increases in greenhouse gases, the more warming that is predicted due to future emissions of greenhouse gases. Of course when making predictions of future climate changes, one does not know the answer beforehand. But if current models are tuned to be overly sensitive to greenhouse gas emissions, then future predictions of increases in surface temperature due to greenhouse gas emissions will be too large.

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. We do not have time to discuss this topic in much detail. If you are interested in the basics of climate model simulations including reasons why the models produce uncertain predictions, you may want to read, Climate Models for the Layman by Dr. Judieth Curry. ATMO 3336 students are not required to read the document.

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 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.

The Intergovernmental Panel on Climate Change (IPCC) has released a set of reports, which comprises the Fifth Assessment Report (AR5) on climate change. Material from the Summary for Policymakers will be referenced in this section. As reported in the 2013 IPCC Report, various modeling studies have suggested that a doubling of atmospheric carbon dioxide from it pre-industrial value of 280 ppm to 560 ppm, or its equivalent by incorporating the effects of increases in other greenhouse gases, will likely increase mean global temperatures between 1.5 and 4.5°C, with a mean value of 3°C. It is also extremely unlikely that the increase in temperature will be less than 1°C and very unlikely greater than 6°C. According to the report, likely means probability >66%, extremely unlikely means <5%, and very unlikely means <10%. Recall that the non-feedback calculation for the increase in global average temperature after carbon dioxide has doubled from 280 ppm to 560 ppm is 1°C. This means that most of the climate models used to produce the 2013 IPCC Report have fairly strong positive feedbacks with respect to changes in carbon dioxide (greenhouse gases), since the most likely outcome (from climate models) is that if CO₂ were doubled, the global average temperature would increase by 3°C, which is much more than 1°C that has been estimated for the "non feedback" case.

Recall from the previous page that most current climate models have a strong positive feedback with respect to temperature changes initiated by increasing greenhouse gases. Specifically, much of the predicted warming in climate model simulations is not directly caused by human emissions of greenhouse gases, but happens as a result of positive feedbacks in the models that are related to changes in water vapor and clouds. As pointed out on the previous page, models are known to problems in accutately representing the movement of water vapor and the formation of clouds. Thus, two process that are not well represented in climate models, i.e., have a lot of uncertainty, end up being responsible for much of the predicted overall warming. The point is that current climate model predictions concerning the magnitude of the overall warming that may result from human emissions of greenhouse gases are far from fact and should be considered uncertain predictions. This is not to say that the net internal feedbacks might not be positive. They may very well be. But there are many who believe that models overestimate the net positive feedback. In other words, the model simulations end up being too sensitive to increases in greenhouse gases and may predict more warming than will take place in the real world. Instuctor's note. In my opinion, the IPCC places too much confidence in climate model simulations and fails to properly convey the uncertainties in the predictions made by those models.

You should understand that these studies are done by instantly doubling carbon dioxide in the model, then letting the model run until a new equilibrium climate state is 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's 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, thus the amount of radiation energy that the planet emits to space starts to increase.
• 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 is able to mix somewhat through 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 1.5-4.5°C (2.7-8.1°F) by 2060, even though some predict that the level of CO₂ will reach 560 ppm 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. This would require us to take CO₂ out of the atmosphere.
2. Even if we were to stabalize greenhouse gas concentrations today by completely eliminating new emissions, this delay means that the surface temperature will continue to warm until a new equilibrium is reached (this may take a hundred years or more).
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.

Model Predictions of Global Average Temperature Changes for the 21^st Century

In the 2013 IPCC Summary for Policymakers it is stated that the global average temperature change for the period 2016 - 2035 relative to the 1986 - 2005 period will likely be in the range 0.3°C to 0.7°C warmer. The models predict an average rate of warming of 0.17°C per decade over this period. The 2007 IPCC report stated 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 [due to the ocean delay]. Therefore, the near term prediction is for warming even if we reduce (or eliminate) emissions of greenhouse gases. The 2013 IPCC document addresses the issue of climate change commitment and irreversibility. If climate model predictions are correct, then each year that CO₂ increases means a commitment to some higher level of temperature in the future, which cannot be easily reduced or reversed without some way to remove large quantities of CO₂ from the atmosphere. Quoting from section E.8 of the 2013 IPCC Summary for Policymakers:

Cumulative emissions of CO₂ largely determine global mean surface warming by the late 21st century and beyond. Most aspects of climate change [that result from human emissions of greenhouse gases] will persist for many centuries even if emissions of CO₂ are stopped. This represents a substantial multi-century climate change commitment created by past, present, and future emissions of CO₂.

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. This can now be compared to the observed value of 0.20°C per decade as determined from satellite measurements (see figures below). The success of this early prediction through the end of the 1990s led many to have confidence in the ability of climate models to predict changes in global average temperture due to increasing greenhouse gases. However, the relatively rapid warming trend that took place in the late 1990s seemed to slow down in the early 2000s, and this was not predicted by most of the IPCC climate models.

The first set of figures below are designed to compare the climate model predictions of global average temperature changes with the observed temperature changes from satellite observations and radiosonde measurements (data collected to make skew-T diagrams). These figures were produced by a climate scientist who believes that climate models overestimate warming due to increased greenhouse gases. Satellite observations of the global average temperature in the lower troposphere have been available since 1979. The figure on the left plots the change in global average temperature in the lower tropospere relative to the average temperature over the five year period from 1979 to 1983. The thin colored lines represent predictions from the 102 climate model runs selected by the IPCC as part of the Coupled Model Intercomparison Project (CMIP5). The CMIP5 model runs are intended to provide the range of climate model projections of climate change. The thick black line is the average of all the CMIP5 model runs. The thick red and blue lines are derived from measured observations from satellites. The thick green line is based heavily on radiosonde data measured by weather balloons around the globe. Note: All the lines on this plot are adjusted so that the net temperature change over the period 1979 to 1983 is zero. A point on any line represents the change in temperature of the model run or dataset relative to its average temperature from 1979 to 1983. This allows us to easily compare the trend or change in temperature. However, this hides the fact that not all models and observational data sets have the same global average temperature over the years from 1979 to 1983.

The figure on the right ranks the trend in the global averaged temperature of the lower troposphere over the period 1979 to 2018 from the 102 CMIP5 climate model runs (in grey) and the three observational datasets (in red, green, and blue). The black bar show the average for the 102 model runs. The temperature trends are measured in degrees Celsius per decade. All of the model runs and observational datasets have positive temperature trends for the 1979 to 2018 time period, i.e., warming trends. Note that all but a few of the model runs have warming trends that are larger than the observational datasets. Figures like these are used to argue that most of the climate model runs that the IPCC uses to predict future changes in temperature predict too much warming when compared with available measurements. In particular, the IPCC often uses the average model trend as the "best" estimate for how temperature will change in the future. One possibility is that climate models are overly sensitive to greenhouse gas increases, which results in predictions of more warming than will actually occur. No one seriously argues that the global average temperature has not been increasing over the period from 1979 to 2018. However, since many models are shown to predict more warming than has been observed over this time period, there is the possibility that future prections of warming will also be too high.

Look again at the figure on the left. There appears to be relatively good agreement between the climate model predictions and observations through the first half of the comparison period, i.e., from 1979 to the late 1990s and perhaps into the early 2000s. After 2000, there is a widening gap between the model predictions and observations with the majority of models predicting more warming than the observations. Most climate models have predicted far too much warming of global average surface temperature since the early 2000s. The observed slowdown in global warming from about 2002 through at least 2013 is sometimes referred to as "the pause in global warming." If you do a web search of that phrase, you will find a spirited debate about its existence. There are several plausible reasons for this discrepancy between climate model predictions and observed temperature changes after 2000. One is that the lack of warming since 2000 is due to natural variability. We know climates are always changing even without considering the influences of humans. There may just be a natural cooling cycle that is masking the model-predicted warming from additional greenhouse gases. A second possibility is that energy or heat is currently being stored in the deep oceans instead of warming the surface temperature. Since the deep ocean has such a high heat capacity, this would not immediately cause much of a change in ocean temperature, but could slow the rise in surface air temperature. It is possible that the models are incorrectly distributing where the extra energy (due to the radiation imbalance resulting from increasing greenhouse gases) is going in the climate system. A third possibility is that the models are wrong. Specifically, the real climate is not as sensitive to human emissions of greenhouse gases as the models predict. In other words, the real climate response to additional carbon dioxide may not be dominated by positive feedbacks.

Changes in global average temperature relative to the 1979 to 1983 average as predicted by 102 CMIP5 climate model runs (thin colored lines), two satellite-derived estimates (bold blue and red lines), and one based on radiosonde measurements (bold green line). The bold black line is the average temperature change for all 90 models. Observed temperature change estimates are through 2018 (Source). Click here to Open a larger image in another tab.

Ranking of the trends for changes in the global averaged temperature in the lower tropoposphere for 102 CMIP5 models (grey lines), two satellite-derived estimates (blue and red lines), and one based on radiosonde measurements (green line). The black line shows the average of the 102 model runs. The left axis (y-axis) is the temperature trend in degrees Celsius per decade for the temperature change from 1979 through 2018. (Source). Click here to Open a larger image in another tab.

The next set of figures (below) are based the latest IPCC report. The IPCC tends to give significant weight to climate model predictions. These figures are slightly different from the ones above. In this case, the climate model runs were forced with the known (measured) increases in greenhouse gases up until the year 2005. After 2005, the models make predictions based on various estimates for how greenhouse gases would increase. The temperature anomaly graphed in these plots is the change in global average surface temperature relative to the global avearage temperature from 1986 to 2005. The portion of the model runs prior to 2005 is known as a "hindcast" since the modelers knew both the actual increase in temperature (from observations) and used the actual increases in greenhouse gases (measured). The individual model runs are shown with thin grey lines. (Note: models were "tuned" to produce a temperature change that was very close to the observed temperature change. This part is not a true forecast.) After 2005, the models used various estimates for how greenhouse gases would increase. The actual temperature change is not known by the modelers, so temperature changes after 2005 are predictions. Individual predictions from the different model runs are shown using thin colored lines. The bold black lines in the figures show the observed change in global average surface temperature based on 4 different observational datasets. The figures below are slightly different than the figures above in that the temperature changes are based on surface (near ground) air temperature and not a temperature averaged over the lower troposphere. The observations are mainly air temperature measured at surface observing stations.

The figure on the left is taken directly from the IPCC report. The report was published in 2013. Thus, the observational data only runs through 2012. The observed change in temperature is near the lower portion of all the model predictions. Some claim this plot is misleading because 2012 was near the end of the "pause in global warming." The figure on the right updates the observational data through September 2020. The observations now contain the warmer years after 2014. Including the years after 2012 does bring the observed changes in temperature more within the range predicted by the model runs, though the observations are still in the lower part of the model prediction spread.

The image on the left is figure 11-25A from the IPCC's Fifth Assessment Report, which shows changes in global average temperature relative to the 1986 to 2005 average (labeled as temperature anomaly). The bold black lines show the the temperature anomaly as determined from four different measured datasets of global average surface temperature. The individual thin lines show the output from climate models run by the IPCC. The model simulations were run in 2005. Model output from 1986 to 2005 (grey lines) are known as "hindcasts" since the simulation covers years that have already passed. Hindcasts are often used to test whether a model was able to closely simulate past times when the temperature changes are known. Since the past temperature changes are known, modelers are able to "tune" their models to match these past changes. The individual model lines are colored after 2005 to indicate that these are true forecasts of future temperature change. The last year for the observational data in this figure is 2012. The figure on the right has been updated to include observational data through September 2020. (Source: the Climate Lab Book)

During and just after the "pause in global warming" from 2002 to 2013, some wondered if the global average temperature would continue to be steady or even turn around and begin a period of cooling. Based on observations, we can now say that the global average temperature has begun to rise again after the pause. The figure below shows the satellite-derived changes in the global average temperature from 1979 through August 2020. The approximate period of the "pause in global warming" is marked in green. The red line shows a smoothed version of the month to month temperature changes. The red line shows that the global average temperature today is about 0.5°C warmer than the global average temperature over the period from 1980 to 2010. The global average temperature is significantly above the level of the pause and near the warmest of any time in the satellite record since 1979.

Monthly changes in the global average temperature of the lower atmosphere based on satellite observations from 1979 to August 2020 relative to the average temperature from 1981 to 2010. Blue circles show the temperature differece each month. The red line is smoothed using a 13 month centered average. The approximate period of the "pause in global warming" in green was added by Dr. Ward. ( Source for the original figure)

Does this prove that climate models have been right and can accurately predict how the global average temperature will increase as greenhouse gases increase in the atmosphere? No, in the sense that we cannot scientifically prove how accurate climate model predictions are using the scientific method. As we will discuss soon, the Earth's climate has changed naturally throughout its history with both warming and cooling periods. There were times in the past when it was much colder than today and times in the past when it was much warmer than today. Changes in greenhouse gas concentrations are just one of many factors that influence the global average temperature. The changes we are observing now are due to a combination of natural climate change and human-caused increases in greenhouse gases. We cannot separate these effects, i.e., we do not know how the climate would have changed if greenhouse gases did not increase. We expect that increasing greenhouse gases will be a warming influence. But is all (or even most) of the recent warming due to greenhouse gas increases? Or is most of the recent warming simply part of a natural cycle of warming without much influence from greenhouse gas increases? The answer to these questions remains uncertain. According to climate models, the recent warming is almost entirely due to greenhouse gas increases because if the same models are run without increasing greenhouse gases, they do not simulate increasing temperature. But if the recent warming is natural and the models incorrectly attribute warming to greenhouse gases, then they will likely overpredict future warming as greenhouse gases continue to increase, i.e., the simulations are overly sensitive to increases in greenhouse gases. The amount of future is warming is very important in deciding future policies regarding greenhouse gas emissions. If the warming from greenhouse gases is small, then perhaps the benefits of using fossil fuels outweighs the harms or risks. However, if the warming from greenhouse gases is large, then the prudent course of action would be to severely reduce future emissions. Based on the presentation above, it seems that most models have been predicting more warming than has been measured. The reason for this discrepancy is not fully understood. The Earth's climate system is very complex and difficult (some say impossible) to accurately model. Will models continue to overpredict future warming? The answer to that question is important but uncertain.

Currently, the Intergovernmental Panel on Climate Change (IPCC) projects a warming of 0.3-4.8°C (0.5-8.6°F) in global average temperature for the last two decades of this century (2081 - 2100) relative to the average temperature for 1985 - 2005. Note that this is toned down somewhat compared with the previous (2007) IPCC report which predicted temperature increases in the range of 1.1-6.4°C. This updated estimate is based on the latest runs of what are considered to be the best global climate models AND the several 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. The figure below shows the future changes in carbon dioxide concentration for the 4 different emission senarios considered by the IPCC. The model predictions shown in the figures above, include all four of the emission senarios shown below.

Future concentrations of greenhouse gases (in units of carbon dioxide equivalent greenhouse forcing) based on the 4 different emission senarios tested by the IPCC. Prediction of future climate changes depend heavily on the emission senario selected, especially after 2030 when the emission senarios diverge greatly.

Climate Model Predictions for Climate Changes at Regional Scales

In previous semesters, I would point out that while all climate models predict warming of the global average temperature (giving us relatively high confidence that human emissions of greenhouse gases will result in global warming, although the magnitude of the warming remains uncertain), individual model predictions are all over the place when you look at regional scale (small spatial scale) predictions for changes in temperature and precipitation (giving us low confidence in the ability of climate models to project regional climate changes). In other words, two models that predict similar changes in global average temperature will often have vastly different predictions for climate changes that might happen over smaller regions, such as the Senoran Desert region for example. In spite of the difficulty in being able to predict climate changes that may occur over regional scales, accurate predictions of climate changes over regional scales are very important in understanding and preparing for how climate change will ultimately impact humans and other life. Life will be more impacted by changes on regional scales where the life exists, rather than changes in global average temperature.

The latest IPCC Report 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. I do not have time to evaluate the IPCC claim that regional projections from climate models are becoming more accurate and trustworthy. Here is a paraphrased summary taken from the most recent IPCC report: In summary, IPCC Fifth Assessment 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 greater over land than ocean, and the high northern hemisphere latitudes (Arctic) will warm more rapidly than the global average. In spite of recent improvements in the regional scale predictions, climate models continue to 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 and regional-scale climates.

Instructor's note. Simulation of regional climate changes continues to be an area of active debate. I attended a seminar recently in which the speaker argued that regional scale climate changes have a significant random component to them, i.e., over short time scales (decades), random and unpredictable changes in circulation patterns have a large influence on regional climate changes. If this is true, there may be a fundamental limit to how well regional scale climate changes can be predicted over a specific decadal time period, and perhaps some are expecting more from climate models with repsect to regional climate change than they can possibly deliver. However, one may be able to separate the random components of regional climate change from the global climate change forced by adding greenhouse gases. This again raises the possibility that climate models may be able to somewhat accurately predict the increase in global average temperature due to increasing greenhouse gases, but have little skill in predicting changes over smaller regional areas. In fact, the speaker was running "ensembles" of climate models, similar to ensemble weather forecasting that we covered earlier in the semester, which indicated a whole range of equally possible outcomes at regional spatial scales as greenhouse gases increase. The point is that we may have to change our expectations about the capabilities of climate models. The models may be able to accurately predict the change in global average temperature, but at best only be able to give a range of possible outcomes at regional scales. This regional uncertainty, of course, presents a problem for those who want to plan ahead for known climate changes. The information most applicable for future planning would be regional scale as opposed to global scale climate changes.

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 latest IPCC 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 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 predicts a very likely increase in extreme precipitation events over most mid-latitude land masses as well as wet tropical areas.

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 might the distribution and intensity of extreme weather events change in response climate change resulting from human activities?