PalaeoQUMP:using palaeodata to reduce uncertainties in climate prediction

Numerical models are the only tool we have to examine the consequences of human activities, such as increasing greenhouse gas concentrations in the atmosphere or widespread changes in land use, on climate. Estimates of the change in global temperature caused by doubling the atmospheric CO2 concentration, based on simulations run by all the major modeling groups worldwide for the last Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), ranged from 1.5 to 4.5 degrees C. This wide range of values for the 'climate sensitivity' to a doubling of CO2 makes it difficult for governments to define and gain wide acceptance for policies to prevent 'dangerous' climate change. Many important processes are described in a simplified way in climate models, because the models would otherwise be too complex to run. It is thought that uncertainties about parameter values assigned to key processes are a major source of uncertainty in model predictions of the response to doubling CO2. The DEFRA-funded project QUMP (Quantifying Uncertainties in Model Prediction) has investigated this by running a series of simulations of the modern climate in which parameter values are systematically altered within plausible ranges based on current knowledge. These simulations are then compared with observations of the recent climate. A probability distribution function is generated from the simulations, with individual runs weighted according to their realism in reproducing the observations. From this comparison, QUMP estimated that the 5-95% probability range for the global temperature change caused by doubling the atmospheric CO2 concentration (i.e. the climate sensitivity) is 2.4 to 5.4 degrees C. This means the constraints supplied by recent observations of climate are insufficient to reduce uncertainties in prediction. A way of improving our estimate of climate sensitivity is to use other information to evaluate the model predictions, e.g. information from the geological past about times when climate was very different from today. Ice sheets covered much of the northern hemisphere and sea level was lower at the last glacial maximum (LGM, 21,000 years ago). The oceans were much colder, sea ice was more extensive and the atmospheric concentration of greenhouse gases was lower than present. These ice sheets had virtually disappeared by 6000 years ago (mid-Holocene, MH), but the distribution of incoming solar energy across the northern hemisphere was increased in summer and decreased in winter compared to today, resulting in increases in seasonal temperature contrast and the strength of the monsoons. Testing climate models under these very different climates should put stronger limits on climate sensitivity. We will run a well-tested climate model, using known changes in solar radiation, ice-sheet distribution, and greenhouse gas concentrations for the LGM and MH, and run the same series of simulations (with different values for key processes) as QUMP has done for modern climate. We will evaluate these simulations using reconstructions of LGM and MH climate. There is abundant evidence from pollen in lake and peat-bog sediments for changes in vegetation patterns caused by changes in winter temperature, the length of the growing season and water availability at the LGM and MH. By forcing a vegetation model to reproduce these vegetation patterns we can derive quantitative estimates of these three aspects of climate. There is abundant evidence in the form of old shorelines and lake sediments for changes in lake area caused by changes in precipitation. Again, by forcing a lake model to reproduce these changes in area we can derive quantitative precipitation estimates. These reconstructions, combined with climate reconstructions based on isotopic or geochemical data, will form targets for our simulations. Our project is intended to provide a better estimate of the climate sensitivity to doubling CO2 in time for the IPCC Fifth Assessment Report.