Post-processing sub-seasonal to seasonal climate forecasts under climate change

Abstract

For managing the impacts of climate variability and change, climate outlooks on sub-seasonal and seasonal timescales are attracting more interest from climate-sensitive communities, such as water resource management, agriculture, and energy. With a profound knowledge of the sources of climate predictability, modelling techniques are rapidly developed for forecasting future climate conditions. Recent advancements are dynamical global climate models (GCMs), which typically integrate atmosphere, land surface, ocean, and sea ice components to comprehensively simulate earth climate system and output a wide array of climate forecasts. However, GCMs usually suffer from long-standing modelling issues, such as systematic errors and the failure of reproducing the observed trends in seasonal climate forecasts. Statistical post-processing techniques are frequently employed to improve forecast performance. Many commonly used methods are found to be effective at removing biases, maximising forecast skill, and improving forecast reliability in terms of ensemble spread, but they are seldom designed to resolve the trend disparity issue in the post-processed climate forecasts. This issue should not be neglected as global and regional land surface temperature and precipitation have shown discernible temporal trends over recent decades. To address this gap, the overarching objective of this thesis is to develop and demonstrate the merit of a new, trend-aware forecast post-processing method that eliminates the trend disparity between climate forecasts and observations while making the forecasts bias-free, skillful, and reliable.The first part of this research aims to develop a new statistical post-processing method to embed the observed trend into seasonal temperature forecasts. I extend the capability of a calibration method, the Bayesian joint probability (BJP) modelling approach, by introducing a new trend component into the algorithm. The new model (named BJP-t) is applied to calibrate January mean forecasts of daily maximum temperatures from the SEAS5 seasonal forecasting system, operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) in three test stations in Australia. In these cases, the BJP-t calibrated forecasts are shown to accurately reproduce the observed trends, and are more skillful, more reliable, and sharper than raw and BJP calibrated forecasts.ii In the BJP-t model, the trend is entirely inferred from the training data. In practice, given limited available periods of retrospective forecasts for model training, these inferred trends are subject to large sampling errors, and may not reflect true underlying trends in the observations. Accordingly, the second part of my thesis further develops the BJP-t model to account for trend uncertainty.The extended trend-aware forecast post-processing method is applied to SEAS5 seasonal mean minimum and maximum temperature forecasts, and the evaluations are upscaled to the Australian continent. After trend-aware post-processing that deals with trend uncertainty...