Research Fellows Directory
Professor Theodore Shepherd FRSC,FAGU,FAMS
University of Reading
Computer simulation models based on fundamental physical laws are our primary tool for predicting climate change. However, the state-of-the-art models exhibit a disturbingly wide range of predictions of future climate change even when forced by the same greenhouse-gas scenarios, especially when examined at the regional scale. The reasons for this are not understood. Moreover the divergence of model projections has not noticeably decreased as the models have become more comprehensive and sophisticated. This represents a basic challenge to our fundamental understanding of climate.
Most attention in climate science has hitherto focused on the thermodynamic aspects of climate, which control global properties such as global-mean temperature and mean sea level. Dynamical aspects, which involve the atmospheric circulation, have received much less attention. However regional climate, including persistent climate regimes and extremes, is strongly controlled by atmospheric circulation patterns, which exhibit chaotic variability in time and whose representation in climate models depends sensitively on parameterised processes. Furthermore the dynamical aspects of climate model projections are much less robust than the thermodynamic ones. There are good reasons to believe that model bias, the divergence of model projections, and chaotic variability are somehow related, although the relationships are not well understood. This calls for studying them together.
This research will provide a sounder scientific basis for interpreting atmospheric circulation aspects of the observational record and model predictions of climate variability and change on a regional scale, thereby better quantifying the sources of uncertainty in those predictions. This area of climate science will become increasingly important in the future to support the rapidly growing societal demand for accurate climate information on various time scales, including variability, with meaningful uncertainties.
Interests and expertise (Subject groups)