The Madden-Julian Oscillation (MJO) is the leading mode of intra-seasonal climate variability, having profound impacts on a range of weather and climate phenomena. Here, we use a wavelet-based spectral Principal Component Analysis (wsPCA) to evaluate the skill of 20 state-of-the-art CMIP6 models in capturing the magnitude and dynamics of the MJO. The advantages of wsPCA are its ability to focus on desired frequencies and capture each propagative physical mode with one principal component (PC). We show that the MJO contribution to the total intra-seasonal climate variability is substantially underestimated in most CMIP6 models. The joint distribution of the modulus and angular frequency of the complex wavelet PC series associated with MJO is used to rank models relatively to the observations through the Wasserstein distance. Using Hovmöller phase-longitude diagrams, we show that precipitation variability associated with MJO is underestimated in most CMIP6 models for the Amazonia, Southwest Africa, and Maritime Continent.

Saltwater intrusion has become one of the most concerning issues in the Vietnamese Mekong Delta (VMD) due to its increasing impacts on agriculture and food security of Vietnam. Reliable estimation of salinity plays a crucial role to mitigate the impacts of saltwater intrusion. This study developed a hybrid technique that merges satellite imagery with numerical simulations to improve the estimation of salinity in the VMD. The salinity derived from Landsat images and by numerical simulations was fused using the Bayesian inference technique. The results indicate that our technique significantly reduces the uncertainties and improves the accuracy of salinity estimates. The Nash-Sutcliffe coefficient is 0.73, which is much higher than that of numerical simulation (0.69) and Landsat estimation (0.67). The correlation coefficient between the merged and measured salinity is relatively high (0.75). The variance of the ensemble salinity errors (2.57 ppt2) is lower than that of Landsat estimation (3.65 ppt2) and numerical simulations (8.69 ppt2). The proposed approach in this study shows a great potential to combine multiple data sources of a variable of interest to improve its accuracy and reliability wherever these data are available.