Description: Extreme climate events and their impacts are currently arising as a critical feature of climate change. Paleoclimate studies are essential for understanding global environmental change and predicting extreme’s trends as the paleo-studies determine the factors that caused changes in the climate. Many studies have suggested that the mid-Pliocene and last interglacial (LIG) can be potentially used as an analogue for the future climates, but the extreme climate events are often missing in these studies. This thesis aims to show whether the LIG and mid-Pliocene are considered as analogues for the future of two extreme climate indices, including summer days index and heavy precipitation index. The MPI-ESM and COSMOS are employed to simulate the LIG, mid-Pliocene, pre-industrial, and future climates. First, the anomalies of temperature, precipitation, and selected indices are plotted for the simulations with respect to PI. In general, the summer days and heavy precipitation patterns are similar to the temperature and precipitation patterns, respectively. The probability density functions of climate variables and extreme indices in the centre of North America and Africa, the south of Africa, and Malaysia, clearly show that the increases in the average temperature and precipitation result in a growth in the corresponding extreme index. Comparing the anomaly plots for different simulations, the LIG can be only considered as analogue for future of summer days index in the northern-hemisphere regions such as the centre of North America. The mid-Pliocene not only is a good analogue for the summer days at the global scale but also can be used regionally for the prediction of heavy precipitation events. Due to the different characteristics of models employed in this project, there are some discrepancies in the results of similar simulations produced by MPI-ESM and COSMOS.

Description: Using the climate model CLIMBER-X, we present an efficient method for assimilating the temporal evolution of surface temperatures for the last deglaciation covering the period 22000 to 6500 years before the present. The data assimilation methodology combines the data and the underlying dynamical principles governing the climate system to provide a state estimate of the system, which is better than that which could be obtained using just the data or the model alone. In applying an ensemble Kalman filter approach, we make use of the advances in the parallel data assimilation framework (PDAF), which provides parallel data assimilation functionality with a relatively small increase in computation time. We find that the data assimilation solution depends strongly on the background evolution of the decaying ice sheets rather than the assimilated temperatures. Two different ice sheet reconstructions result in a different deglacial meltwater history, affecting the large-scale ocean circulation and, consequently, the surface temperature. We find that the influence of data assimilation is more pronounced on regional scales than on the global mean. In particular, data assimilation has a stronger effect during millennial warming and cooling phases, such as the Bølling-Allerød and Younger Dryas, especially at high latitudes with heterogeneous temperature patterns. Our approach is a step toward a comprehensive paleo-reanalysis on multi-millennial time scales, including incorporating available paleoclimate data and accounting for their uncertainties in representing regional climates.