How are seasonal prediction skills related to models’ performance on mean state and annual cycle?

Given observed initial conditions, how well do coupled atmosphere–ocean models predict precipitation climatology with 1-month lead forecast? And how do the models’ biases in climatology in turn affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981–2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models’ ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian–Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.     

Climate change detection and attribution in the Ganga-Brahmaputra-Meghna river basins

Ganga-Brahmaputra-Meghna(GBM) river basin is the third-largest and one of the most populated river basins in the world. As climate change is affecting most of the hydrometeorological variables across the globe, this study investigated the existence of climate change signal in all four climatological seasons in the GBM river basin and assessed the contribution of anthropogenic activities, i.e., Greenhouse Gases(GHGs) emission in the change. Significant decreasing trends in the monsoon and a small increase in pre-monsoon precipitation were observed. Negligible change was detected in post-monsoon and winter season precipitation. CMIP5 GCMs were used for climate change detection, change point estimation, and attribution studies. Support Vector Machine(SVM) regression method was adopted to downscale GCM variables at the local scale. Monte-Carlo simulation approach was used to detect changes in different seasons. The climate change ‘signals' were detectable after the year 1980 using Signal to Noise ratio(SNR)method in the majority of central and north-western regions. The change point was detectable only in annual monsoon precipitation at the basin level. Attribution analysis indicated 50% contribution of anthropogenic activities(GHGs) to annual monsoon precipitation changes. So, there is high confidence that monsoon precipitation in GBM has significantly changed due to anthropogenic activities. Different mitigation and adaption measures are also suggested, which may be adopted to manage the growing demand and water availability in the basin.