Preface

This Intergovernmental Panel on Climate Change (IPCC) Technical Paper on "An Introduction to Simple Climate Models used in the IPCC Second Assessment Report" is the second paper in the IPCC Technical Paper series and was produced in response to a request made by the Subsidiary Body for Scientific and Technological Advice (SBSTA) of the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC).

Technical Papers are initiated either at the request of the bodies of the COP, and agreed by the IPCC Bureau, or as decided by the IPCC. They are based on the material already in IPCC Assessment Reports and Special Reports and are written by Lead Authors chosen for the purpose. They undergo a simulta neous expert and government review, during which comments on this Paper were received from 81 reviewers from 26 countries, followed by a final government review. The Bureau of the IPCC acts in the capacity of an editorial board to ensure that review comments have been adequately addressed by the Lead Authors in the finalization of the Technical Paper.

The Bureau met in its Twelfth Session (Geneva, 3-5 February 1997) and considered the major comments received during the final government review. In the light of its observations and requests, the Lead Authors finalized the Technical Paper. The Bureau was satisfied that the agreed Procedures had been followed and authorized the release of the Paper to the SBSTA and thereafter publicly.

We owe a large debt of gratitude to the Lead Authors who gave of their time very generously and who completed the Paper at short notice and according to schedule. We thank the Co-chairmen of Working Group I of the IPCC, John Houghton and Gylvan Meira Filho who oversaw the effort, the staff of the United Kingdom Meteorological Office graphics studio who prepared the figures for publication and particularly David Griggs, Kathy Maskell and Anne Murrill from the IPCC Working Group I Technical Support Unit, for their insistence on adhering to quality and timeliness.

B. Bolin

Chairman of the IPCC

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N. Sundararaman Secretary of the IPCC

This Technical Paper is intended as a primer on the climate system and simple climate models (SCMs), and has two objectives: (a) to explain how SCMs work, the processes that are included in them, what their strengths and weaknesses are in relation to more complex models, the purposes to which they are applied, and why they have been used extensively in the Working Group I volume of the IPCC Second Assessment Report (IPCC WGI, 19961); and (b) to fully document the procedures and assumptions used to generate the trace gas concentration, global mean temperature change, and global mean sea level rise projections presented in the SAR WGI (Section 6.3) and in the IPCC Technical Paper on Stabilization of Atmospheric Greenhouse Gases: Physical, Biological and Socio-economic Implications (IPCC TP STAB, 1997).

The major components of the climate system that are important for climatic change and its consequences, such as sea level rise, during the next century are: the atmosphere, oceans, terrestrial biosphere, glaciers and ice sheets and land surface. In order to project the impact of human perturbations on the climate system, it is necessary to calculate the effects of all the key processes operating in these climate system components and the interactions between them. These climate processes can be represented in mathematical terms based on physical laws such as the conservation of mass, momentum, and energy. However, the complexity of the system means that the calculations from these mathematical equations can be performed in practice only by using a computer. The mathematical formulation is therefore implemented in a computer program, which we refer to as a "model". If the model includes enough of the components of the climate system to be useful for simulating the climate, it is commonly called a "climate model". Climate system models are fundamentally different from statistical models used in some of the social sciences, which are based purely on empiri cal correlations and are unrelated to an underlying body of physical law.

The climate system can be represented by models of varying complexity, i.e., for any one component of the climate system a hierarchy of models can be identified. The main differences between models within a given hierarchy are:

The number of spatial dimensions in the model. In a model it is necessary to represent physical quantities which vary continuously in space (e.g., temperature, humidity and wind speed) by their values at a finite number of points. The spacing between the points of the grid is the "spatial resolution". In the most complex models of the atmosphere and ocean used to study climate (referred to as atmosphereocean general circulation models, or AOGCMs), such quantities are represented by a three-dimensional (longitude-latitude-height) grid with typical horizontal resolutions

of several hundred kilometres. Simpler climate models may represent these physical quantities as averages over one or more spatial dimensions. Instead of, for instance, a three-dimensional grid, one might use a two-dimensional (latitude-height) grid, with each point being an average over all longitudes at a given latitude and height.

The extent to which physical processes are explicitly represented. Even the most complex climate models used to project climate over the next century (AOGCMs) have a typical resolution of hundreds of kilometres in the horizontal. Many important elements of the climate system (e.g., clouds, land surface) have scales that are much smaller than this in reality. Detailed models at high resolution are available for such processes by themselves, but these are computationally too expensive to be included in a climate model. Instead, the climate model has to represent the effect of these sub-grid scale processes on the climate system at its coarse grid scale. A formulation of the effect of a small-scale process on the large-scale is called a "parametrization" (SAR WGI: Section 1.6.1). When the dimensionality of the model is reduced as described above, more processes have to be parametrized.

• The level at which empirical parametrizations are involved. All models rely on parametrization to represent those processes which are not explicitly represented by the model grids. The important difference between models of varying resolution and dimensionality, therefore, is the level at which parametrizations are introduced, not the need for parametrization. However, even in three dimensional AOGCMs, the large-scale behaviour of the model and the nature of processes that are explicitly computed (e.g., winds and ocean currents) can be strongly influenced by the way in which subgrid scale processes are parametrized.

• The computational cost of running the model. SCMs are computationally more efficient than more complex models and can therefore be used to investigate future climate change in response to a large number of different scenarios of future greenhouse gas emissions. Such scenario analysis would be impractical with AOGCMs.

Climate models may also vary in their comprehensiveness i.e., in the number of climate components that are represented. For example, a climate model may try to model only the atmosphere, while a more comprehensive model might include the atmosphere (and atmospheric chemistry), the oceans and the terrestrial and marine biospheres.

In this report, we use the term "simple climate model" (SCM) to refer to the simplified models used in the SAR WGI (Sections 6.3, 7.5.2 and 7.5.3) to provide projections of global mean temperature and sea level change response to the IS92 emissions scenarios and the carbon dioxide (CO2) stabilization

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profiles. The SCMs contain modules that calculate: (a) the concentrations of greenhouse gases for given future emissions; (b) the radiative forcing resulting from the computed greenhouse gas concentrations and aerosol precursor emissions; (c) the global mean temperature response to the computed radiative forcing; and (d) the sea level rise due to thermal expansion of sea water and the response of glaciers and ice sheets. These steps are briefly elaborated upon below.

Emissions to Concentrations

The calculation of future concentrations of greenhouse gases from given emissions entails modelling the processes that transform and remove the different gases from the atmosphere. For example, future concentrations of CO2 were calculated in SAR WGI using models of the carbon cycle which include representations of the exchanges of CO2 between the atmosphere and the oceans and terrestrial biosphere. Other greenhouse gases, rather than being exchanged between different reservoirs, are destroyed through chemical reactions. Concentrations can be derived from emissions using quite simple equations in SCMs once the atmospheric lifetimes of the gases are determined from more complex two- and three-dimensional atmospheric chemistry models.

Concentrations to Global Mean Radiative Forcing

Given the concentrations of globally uniform greenhouse gases, the direct global mean radiative forcing can be computed using simple formulae which provide a close fit to the results of detailed radiative transfer calculations. In the case of tropospheric ozone, the picture is complicated by the fact that this gas is produced from emissions of precursor gases through chemical reactions and its concentration is highly variable in space and time. In this case, concentrations are not directly computed and the radiative forcing is assumed to change based on simple linkages to other gases as a proxy for the full chemistry. Similarly, the radiative forcing due to depletion of stratospheric ozone is directly computed based on a simple relationship to emissions of chlorine and bromine containing chemicals, which has been calibrated based on the results of detailed models. Finally, the amount of aerosol in the lower atmosphere responds essentially instantaneously to changes in emissions because of the short lifetime of aerosols, so specification of an emission scenario amounts to specifying a concentration scenario. Hence, in the SCMs used in SAR WGI, global aerosol emissions are directly linked to global mean radiative forcing (both the direct and indirect components) using the results of three dimensional atmospheric general circulation models (AGCMs) which attempt to represent explicitly the processes determining the amount, distribution, and properties of aerosols in the atmosphere, and the resulting global mean forcing. These processes are poorly understood and the resultant forcings highly uncertain.

Global Mean Radiative Forcing to Global Mean Temperature

Given a scenario of global mean radiative forcing, the next step is to compute the resultant time-varying ("transient") climatic response. This depends both on the climate sensitivity and on the rate of absorption of heat by the oceans. The climate sensitivity is a measure of the global surface temperature change for a given radiative forcing and encompasses the complexity of processes responsible for the way the climate system responds to a radiative forcing, including feedback processes involving, for example, clouds, sea ice and water vapour.

The response of the SCM, for a given scenario of future greenhouse gas and aerosol precursor emissions, is governed by the climate sensitivity and a small number of parameters which control the uptake of heat by the oceans. The climate sensitivity can be estimated by four independent methods: (a) from simulations with three-dimensional AGCMs; (b) from direct observations, at the relevant temporal and spatial scales, of the key processes that determine radiative damping to space and hence climate sensitivity; (c) from reconstructions of radiative forcing and climate response of ancient (palaeo-) climates; and (d) from comparisons of ocean/climate model runs with historical global temperature records.

The climate module of the SCM only provides information about global mean temperature. For information about regional climate change, changes in other variables (e.g., precipitation), and changes in variability and extremes, three-dimensional AOGCMs are required.

Global Mean Temperature to Global Mean Sea Level Rise

Global mean sea level rise in SCMs is computed based on contributions from: (a) the thermal expansion of sea water, which depends on the evolving profile of temperature change in the ocean; and (b) glaciers, small ice-caps and ice sheets, the contributions of which are computed using simple models of these components that are driven by the global mean temperature change as computed by the SCM.

The single largest source of uncertainty in projections of future, time-dependent global mean temperature change is the equilibrium climate sensitivity, which is expected to fall within 1.5 to 4.5°C for a CO2 doubling. SCMs assume that the global mean temperature response to a radiative forcing perturbation depends only on the global mean value of the perturbation, and that the climate sensitivity is the same irrespective of the magnitude or direction of the radiative forcing. The dependence of climate sensitivity on the magnitude, direction, and nature of the forcing is thought to be small, in most cases, compared to the underlying uncertainty in the climate sensitivity itself (a factor of three).

The equilibrium climate sensitivity is also the single most important source of uncertainty for projections of global mean sea level rise, although the variation of temperature change with depth in the ocean and the response of glaciers and ice sheets are also important sources of uncertainty. With regard to the build-up of carbon dioxide in the atmosphere, the largest uncertainties involve interactions between the terrestrial biosphere and climate. The uncertainties in the estimated build-up of atmospheric CO2 are thought to be small for projections spanning two to three decades, but are substantially larger for longer projections.

Both simple and complex models have important roles to play in enhancing our understanding of the range of possible future climatic changes, their impacts, and interactive effects. The more complex models are especially suited for studying those fundamental processes which are resolved by complex models but not by simple models. They also have the potential to provide credible projections of regional scale changes in climatic means and variability. Simple models can be formulated to replicate the global scale average behaviour of complex models and can be calibrated to global scale observations. Due to their computational efficiency and conceptual clarity, simple models are useful for global change scenario development and analysis, and for investigating the interactive effect of subsys tem properties. The use of AOGCMs for the simulation of regional, time-varying climatic change, and the use of SCMs for more extensive sensitivity and scenario analysis, are both

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dictated by pragmatic considerations involving computer resources and the level of detail appropriate when coupling various components together. A long-term goal of Earth system science is the development of increasingly sophisticated coupled models of the climate system.

All climate system models used in the SAR WGI have been tested for their ability to reproduce key features of the existing climate, as well as historical and palaeo-climatic changes. While the validity of these models cannot be proven for future conditions, their ability to recover a variety of observed features of the atmosphere/ocean/biosphere system and observed changes during the recent past supports their use for projections of future climatic change.

However, many uncertainties remain regarding the modelling of the climate system. There is considerable uncertainty about the changes that might occur in some climate system processes, such as those involving clouds, in an altered climate. The effect of aerosols on the radiation balance of the climate is also not well known. Difficult-to-predict changes in the ocean circulation could have a significant effect on both regional and global climatic changes. Unexpected changes in the flow of carbon between the atmosphere and terrestrial biosphere and/or the oceans could occur. Nevertheless, research continues to improve our basic understanding of important processes and their representation in models.