The future of climate modelling

Very little has been said in this chapter about the biosphere. Chapter 3 referred to comparatively simple models of the carbon cycle which include chemical and biological processes and simple non-interactive descriptions of atmospheric processes and ocean transport. The large three-dimensional global circulation climate models described in this chapter contain a lot of dynamics and physics but no interactive chemistry or biology. As the power of computers has increased, it has become possible to incorporate into the physical and dynamical models some of the biological and chemical processes that make up the carbon cycle and the chemistry of other gases. These are enabling studies to be made of the detailed processes and interactions that occur in the complete climate system.

Climate modelling continues to be a rapidly growing science. Although useful attempts at simple climate models were made with early computers it is only during the last 20 years that computers have been powerful enough for coupled atmosphere-ocean models to be employed for climate prediction and that their results have been sufficiently comprehensive and credible for them to be taken seriously by policymakers. The climate models which have been developed are probably the most elaborate and sophisticated of computer models developed in any area of natural science. Further, climate models that describe the natural science of climate are now being coupled with socio-economic information in integrated assessment models (see box in Chapter 9, page 280).

As the power of computers increases it becomes more possible to investigate the sensitivity of models by running a variety of ensembles that include different initial conditions, model parameterisations and formulations. A particularly interesting project31 involves thousands of computer users around the world in running state-of-the-art climate prediction models on their home, school or work computers. By collating data from thousands of models it is generating the world's largest climate modelling prediction experiment.

This chapter has mainly concentrated on modelling the climate response to anthropogenic forcing up to a century or so into the future taking into account what have been called the fast feedbacks. However, questions are increasingly being asked about what is likely to happen to the climate in the longer term as a result of human activities now. Much of the response to change by three components of the climate system, namely the oceans, ice sheets and land surface (e.g. vegetation), occurs over longer time scales than a century (cf Table 7.5). Associated with these are slow feedbacks32 that tend to be non-linear. Even if the atmospheric composition is stabilised, warming of the deeper oceans will occur up to at least 1000 years and major changes of the polar ice caps could occur over many millennia. Because of the magnitude of these changes, their non-linearity and their large impact even in the relatively short term, for instance over sea level rise, it is vital that they are better understood. Much future research on past climates, modelling and observations will be concerned with the characteristics of both fast and slow responses within the climate system.

SUMMARY

This chapter has described the basis, assumptions, methods and development of computer numerical modelling of the atmosphere and the climate. Over the past 30 years alongside the rapid development in the performance and speed of computers, there has been enormous development in the sophistication, skill and performance of atmosphere-ocean coupled general circulation models of the climate. Crucial has been the careful incorporation of the variety of positive and negative feedback processes. Confidence in the ability of models to provide useful projections of future climate is based on model simulations that have been validated against:

• detailed observations of current and recent climate of both oceans and atmosphere

• detailed observations of particular climatic cycles such as El Niño events

• observations of perturbations arising from particular events such as volcanic eruptions

• palaeoclimate information from past climates under different orbital forcing.

A great deal remains to be done to narrow the uncertainty of model predictions. The modelling of cloud feedback processes remains the source of the largest uncertainty. Other priorities are to improve the modelling of ocean processes and the ocean-atmosphere interaction. Larger and faster computers continue to be required for these and also to improve the resolution of regional models. More thorough observations of all components of the climate system also continue to be necessary, so that more accurate validation of the model formulations can be achieved. Very substantial national and international programmes are under way to address all these issues.

QUESTIONS

1 Make an estimate of the speed in operations per second of Richardson's 'people' computer. Where does it fall in Figure 5.1?

2 If the spacing between the grid points in a model is 100 km and there are 20 levels in the vertical, what is the total number of grid points in a global model? If the distance between grid points in the horizontal is halved, how much longer will a given forecast take to run on the computer?

3 Take your local weather forecasts over a week and describe their accuracy for 12, 24 and 48 hours ahead.

4 Estimate the average energy received from the Sun over a square region of the ocean surface, one side of the square being a line between northern Europe and Iceland. Compare with the average transport of energy into the region by the North Atlantic Ocean (Figure 5.16).

5 Take a hypothetical situation in which a completely absorbing planetary surface at a temperature of 280 K is covered by a non-absorbing and non-emitting atmosphere. If a cloud which is non-absorbing in the visible part of the spectrum but completely absorbing in the thermal infrared is present above the surface, show that its equilibrium temperature will be 235 K (=280/20 25 K).33 Show also that if the cloud reflects 50% of solar radiation, the rest being transmitted, the planet's surface will receive the same amount of energy as when the cloud is absent. Can you substantiate the statement that the presence of low clouds tends to cool the Earth while high clouds tend towards warming of it?

Associated with the melting of sea-ice which results in increased evaporation from the water surface, additional low cloud can appear. How does this affect the ice-albedo feedback? Does it tend to make it more or less positive? Estimate from information in Chapter 3 the average net radiative forcing from 1960 to 2003. Compare this with the average heating rate at the Earth's surface deduced from measurements of the energy absorbed by the ocean as detailed on page 127. Comment on your results. A change in radiative forcing at the top of the atmosphere of about 3 W m-2 leads to a change in surface temperature of around 1 °C providing nothing else changes. Consider the plots shown in Figure 5.15b. What surface temperature change would be expected following the Pinatubo volcano? Compare with the information in Fig 5.21 and comment on your comparison. It is through the analysis of data of the kind illustrated in Figure 5.15a and b that the magnitude and sign of cloud-radiation feedback can be studied. Specify the requirements of a programme of measurements in terms of accuracy (in W m-2) and coverage in both space and time if meaningful conclusions about cloud-radiation feedback are to be obtained. It is sometimes argued that weather and climate models are the most sophisticated and soundly based models in natural science. Compare them (e.g. in their assumptions, their scientific basis, their potential accuracy, etc.) with other computer models with which you are familiar both in natural science and social science (e.g. models of the economy).

^ FURTHER READING AND REFERENCE

Solomon, S., Qin, D., Manning M., Chen, Z., Marquis, M., Averyt, K. B., Tigor, M.,

Miller, H. L. (eds.) 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press. Technical Summary (summarises basic information about modelling and its applications)

Chapter 1 Historical overview of climate change science Chapter 8 Climate models and their evaluation Chapter 9 Understanding and attributing climate change Chapter 10 Global climate projections Chapter 11 Regional climate projections

McGuffie, K., Henderson-Sellers, A. 2005. A Climate Modeling Primer, third edition. New York: Wiley.

Houghton, J.T. 2002. The Physics of Atmospheres, third edition. Cambridge: Cambridge University Press.

Palmer, T., Hagedorn, R. (eds.) 2006. Predictability of Weather and Climate. Cambridge: Cambridge University Press.

NOTES FOR CHAPTER 5

1 Richardson, L. F. 1922. Weather Prediction by Numerical Processes. Cambridge: Cambridge University Press. Reprinted by Dover, New York, 1965.

2 For more details see, for instance, Houghton, The Physics of Atmospheres.

3 See Palmer, T. N. 2006, Chapter 1 in Palmer, T., Hagedorn, R. (eds.) 2006 Predictability of Weather and Climate. Cambridge: Cambridge University Press.

4 For more detail see: Chapter 13 in Houghton, The Physics of Atmospheres; Palmer, T. N. 1993. A nonlinear perspective on climate change. Weather, 48, 314-26; Palmer and Hagedorn (eds.), Predictability of Weather and Climate.

5 An equation such as y = ax + b is linear; a plot of y against x is a straight line. Examples of non-linear equations are y = ax2 + b or y + xy = ax + b; plots of y against x for these equations would not be straight lines. In the case of the pendulum, the equations describing the motion are only approximately linear for very small angles from the vertical where the sine of the angle is approximately equal to the angle; at larger angles this approximation becomes much less accurate and the equations are non-linear.

6 Palmer, T. N., Chapter 1 in Palmer and Hagedorn (eds.), Predictability of Weather and Climate.

7 Named 'Southern Oscillation' by Sir Gilbert Walker in 1928.

8 Folland, C. K., Owen, J., Ward, M. N., Colman, A. 1991. Prediction of seasonal rainfall in the Sahel region using empirical and dynamical methods. Journal of Forecasting, 10, 21-56.

9 See for instance Shukla, J., Kinter III, J. L., Chapter 12 in Palmer and Hagedorn (eds.), Predictability of Weather and Climate.

10 Xue, Y. 1997. Biospheric feedback on regional climate in tropical north Africa. Quarterly Journal of the Royal Meteorological Society, 123, 1483-515.

11 For a review of climate feedback processes see Bony, S. et al., 2006. How well do we understand and evaluate climate change feedback processes? Journal of Climate, 19, 3445-82.

12 Associated with water vapour feedback is also lapse rate feedback which occurs because, associated with changes of temperature and water vapour content in the troposphere, are changes in the average lapse rate (the rate of fall of temperature with height). Such changes lead to this further feedback, which is generally much smaller in magnitude than water vapour feedback but of the opposite sign, i.e. negative instead of positive. Frequently, when overall values for water vapour feedback are quoted the lapse rate feedback has been included. For more details see Houghton, The Physics of Atmospheres.

13 Lindzen, R. S. 1990. Some coolness concerning global warming. Bulletin of the American Meteorological Society, 71, 288-99. In this paper, Lindzen queries the magnitude and sign of the feedback due to water vapour, especially in the upper troposphere, and suggests that it could be much less positive than predicted by models and could even be slightly negative. Much has been done through observational and modelling studies to investigate the likely magnitude of water vapour feedback. More detail can be found in Stocker, T. F. et al., Physical climate processes and feedbacks, Chapter

7 in Houghton et al. (eds.), Climate Change 2001: The Science Basis. The conclusion of that chapter, whose authors include Lindzen, is that 'the balance of evidence favours a positive clear-sky water vapour feedback of a magnitude comparable to that found in simulations'.

14 See Figure 2.8 and the definition of radiative forcing at the beginning of Chapter 3.

15 From Figure 8.14 in Randall, D., Wood, R. A. et al. Climate Models and their Evaluation, Chapter 8 in Solomon et al. (eds.) Climate Change 2007: The Physical Science Basis.

16 Note that the variance in the total is less than the sum of the variances of the three parameters. The total is obtained by first adding the values of the parameters from individual model runs.

17 For a description of a recent model and how it performs see Pope, V. et al. 2007. The Met Office Hadley Centre climate modelling capability: the competing requirements for improved resolution, complexity and dealing with uncertainty. Philosophical Transactions of the Royal Society A, 365, 2635-2657.

18 Randall et al. Chapter 8, in Solomon et al. (eds.) Climate Change 2007: The Physical Science Basis.

19 For a recent review see Cane, M. A. et al. 2006. Progress in paleoclimate modeling. Journal of Climate 19, 5031-57.

20 Graf, H.-E. et al. 1993. Pinatubo eruption winter climate effects: model versus observations. Climate Dynamics, 9, 61-73.

21 See Policymakers' summary. In Houghton, J. T., Jenkins, G. J., Ephraums, J. J. (eds.) 1990. Climate Change: The IPCC Scientific Assessment. Cambridge: Cambridge University Press.

22 See Summary for policymakers. In Houghton, J. T., Meira Filho, L. G., Callander, B. A., Harris,

N., Kattenberg, A., Maskell, K. (eds.) 1996. Climate Change 1995: The Science of Climate Change. Cambridge: Cambridge University Press.

23 Detection is the process demonstrating that an observed change is significantly different (in a statistical sense) than can be explained by natural variability. Attribution is the process of establishing cause and effect with some defined level of confidence, including the assessment of competing hypotheses. For further information about detection and attribution studies see

Mitchell, J. F. B., Karoly, D.J. et al. 2001. Detection of climate change and attribution of causes, Chapter 12 in Houghton et al. (eds.), Climate Change 2001: The Scientific Basis, and Hegerl, G. C., Zwiers, F. W. et al. Understanding and attributing climate change, Chapter 9, in Solomon et al. (eds.) Climate Change 2007: The Physical Science Basis.

24 Summary for policymakers. In Houghton et al. (eds.) Climate Change 2001: The Scientific Basis.

25 Summary for policymakers, in Solomon et al. (eds.) Climate Changes 2007: The Physical Science Basis. The definitions of likely, very likely, etc. are given in Note 1 to Chapter 4.

26 Bindoff, N. Willebrand, J. et al. 2007. Observations: Oceanic climate change and sea level, Chapter 5, in Solomon et al. (eds.) Climate Change 2007: The Physical Science Basis.

27 See Gregory, J. et al. 2002. Journal of Climate, 15, 3117-21.

28 From Barnett, T. P. et al, 2005, Science 309, 284-287; modelling simulations from the Hadley Centre UK.

29 The regional scale is defi ned as describing the range of 104 to 107 km2. The upper end of the range (107 km2) is often described as a typical sub-continental scale. Circulations at larger than the sub-continental scale are on the planetary scale.

30 For more information see Giorgi, F., Hewitson, B. et al. 2001, Regional climate information - evaluation and projections. Chapter 10, in Houghton et al. (eds.), Climate Change 2007: The Scientific Basis.

31 See www.climateprediction.net

32 The terminology of fast and slow feedbacks has been introduced by James Hansen - see his Bjerknes Lecture at American Geophysical Union, 17 December 2008 at www.columbia.edu/~jeh1/2008/ AGUBjerknes_20081217.pdf.

33 Hint: recall Stefan's blackbody radiation law that the energy emitted is proportional to the fourth power of the temperature.

NASA'S 2006 CloudSat (artist's rendition) studies the role of clouds and aerosols in regulating the Earth's weather, climate and air quality

THE LAST chapter explained that the most effective tool we possess for the prediction of future climate change due to human activities is the climate model. This chapter will describe the predictions of models for likely climate change during the twenty-first century. It will also consider other factors that might lead to climate change and assess their importance relative to the effect of greenhouse gases.

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