Forecast skill of Bangladesh summer monsoon rainfall in C3S and NMME models after calibration

Abstract

This study assesses the dynamical seasonal predictions initialized in April and May for forecasting Bangladesh summer (June to September: JJAS) monsoon rainfall (BSMR) over the 1993–2016 period. The BSMR from nine models, sourced from the North American Multi-Model Ensemble (NMME) and the Copernicus Climate Change Service (C3S), undergoes a calibration process. This calibration employs the Canonical Correlation Analysis (CCA) technique to rectify biases within each individual model's ensemble mean BSMR data (referred as predictor or X variable). These corrections are made in comparison to observed BSMR (referred as predictand or Y variable), acquired from the Climate Hazards Group InfraRed Precipitation with Station data. Subsequently, the models that undergo calibration are amalgamated to construct a calibrated multi-model ensemble (CMME), which, in turn, facilitates the generation of a forecast for BSMR. The CCA correction brings about a significant improvement in root-mean-square error, underscoring the presence of correctable systematic biases in the raw model forecasts. However, these CCA corrections weakly enhance the skill (anomaly correlation) across the region. The scores that assess discrimination (the two-alternative forced-choice: 2AFC and the area under the relative operating characteristic curve: ROC for above/below normal BSMR) for tercile-based forecasts exceeded 50 % across a substantial portion of the region. This indicates a superior level of discrimination compared to what one would anticipate based on climatology. Using the CMME approach, a probabilistic forecast for the 2022 BSMR was generated and proved quite effective in capturing the observed 2022 BSMR tercile, which includes below-normal and above-normal categories of rainfall in the central-southern and northern regions of Bangladesh respectively. Furthermore, the absence of substantial skill improvements may be attributed to inaccuracies in the teleconnection patterns of the simulated first leading principal component (PC) time series of BSMR with the El Niño Southern Oscillation. In contrast, the second PC time series exhibits a similar connection to observations. These findings emphasize the importance and utility of statistical post-processing in producing reliable seasonal climate outlooks for the region.