Going with the trend: forecasting seasonal climate conditions under climate change
AbstractFor managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast post-processing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical post-processing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast post-processing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting post-processed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast post-processing is expected to boost user confidence in seasonal climate forecasts.