6.2.2 Methods – Palaeoclimate Modelling

Climate models are used to simulate episodes of past climate (e.g., the Last Glacial Maximum, the last interglacial period or abrupt climate events) to help understand the mechanisms of past climate changes. Models are key to testing physical hypotheses, such as the Milankovitch theory (Section 6.4, Box 6.1), quantitatively. Models allow the linkage of cause and effect in past climate change to be investigated. Models also help to fill the gap between the local and global scale in palaeoclimate, as palaeoclimatic information is often sparse, patchy and seasonal. For example, long ice core records show a strong correlation between local temperature in Antarctica and the globally mixed gases CO₂ and methane, but the causal connections between these variables are best explored with the help of models. Developing a quantitative understanding of mechanisms is the most effective way to learn from past climate for the future, since there are probably no direct analogues of the future in the past.

At the same time, palaeoclimate reconstructions offer the possibility of testing climate models, particularly if the climate forcing can be appropriately specified, and the response is sufficiently well constrained. For earlier climates (i.e., before the current ‘Holocene’ interglacial), forcing and responses cover a much larger range, but data are more sparse and uncertain, whereas for recent millennia more records are available, but forcing and response are much smaller. Testing models with palaeoclimatic data is important, as not all aspects of climate models can be tested against instrumental climate data. For example, good performance for present climate is not a conclusive test for a realistic sensitivity to CO₂ – to test this, simulation of a climate with a very different CO₂ level can be used. In addition, many parametrizations describing sub-grid scale processes (e.g., cloud parameters, turbulent mixing) have been developed using present-day observations; hence climate states not used in model development provide an independent benchmark for testing models. Palaeoclimate data are key to evaluating the ability of climate models to simulate realistic climate change.

In principle the same climate models that are used to simulate present-day climate, or scenarios for the future, are also used to simulate episodes of past climate, using differences in prescribed forcing and (for the deep past) in configuration of oceans and continents. The full spectrum of models (see Chapter 8) is used (Claussen et al., 2002), ranging from simple conceptual models, through Earth System Models of Intermediate Complexity (EMICs) and coupled General Circulation Models (GCMs). Since long simulations (thousands of years) can be required for some palaeoclimatic applications, and computer power is still a limiting factor, relatively ‘fast’ coupled models are often used. Additional components that are not standard in models used for simulating present climate are also increasingly added for palaeoclimate applications, for example, continental ice sheet models or components that track the stable isotopes in the climate system (LeGrande et al., 2006). Vegetation modules as well as terrestrial and marine ecosystem modules are increasingly included, both to capture biophysical and biogeochemical feedbacks to climate, and to allow for validation of models against proxy palaeoecological (e.g., pollen) data. The representation of biogeochemical tracers and processes is a particularly important new advance for palaeoclimatic model simulations, as a rich body of information on past climate has emerged from palaeoenvironmental records that are intrinsically linked to the cycling of carbon and other nutrients.