Description: The tropical oceans have long been recognized as the most important region for large-scale ocean–atmosphere interactions, giving rise to coupled climate variations on several time scales. During the Tropical Ocean Global Atmosphere (TOGA) decade, the focus of much tropical ocean research was on understanding El Niño–related processes and on development of tropical ocean models capable of simulating and predicting El Niño. These studies led to an appreciation of the vital role the ocean plays in providing the memory for predicting El Niño and thus making seasonal climate prediction feasible. With the end of TOGA and the beginning of Climate Variability and Prediction (CLIVAR), the scope of climate variability and predictability studies has expanded from the tropical Pacific and ENSO-centric basis to the global domain. In this paper the progress that has been made in tropical ocean climate studies during the early years of CLIVAR is discussed. The discussion is divided geographically into three tropical ocean basins with an emphasis on the dynamical processes that are most relevant to the coupling between the atmosphere and oceans. For the tropical Pacific, the continuing effort to improve understanding of large- and small-scale dynamics for the purpose of extending the skill of ENSO prediction is assessed. This paper then goes beyond the time and space scales of El Niño and discusses recent research activities on the fundamental issue of the processes maintaining the tropical thermocline. This includes the study of subtropical cells (STCs) and ventilated thermocline processes, which are potentially important to the understanding of the low-frequency modulation of El Niño. For the tropical Atlantic, the dominant oceanic processes that interact with regional atmospheric feedbacks are examined as well as the remote influence from both the Pacific El Niño and extratropical climate fluctuations giving rise to multiple patterns of variability distinguished by season and location. The potential impact of Atlantic thermohaline circulation on tropical Atlantic variability (TAV) is also discussed. For the tropical Indian Ocean, local and remote mechanisms governing low-frequency sea surface temperature variations are examined. After reviewing the recent rapid progress in the understanding of coupled dynamics in the region, this study focuses on the active role of ocean dynamics in a seasonally locked east–west internal mode of variability, known as the Indian Ocean dipole (IOD). Influences of the IOD on climatic conditions in Asia, Australia, East Africa, and Europe are discussed. While the attempt throughout is to give a comprehensive overview of what is known about the role of the tropical oceans in climate, the fact of the matter is that much remains to be understood and explained. The complex nature of the tropical coupled phenomena and the interaction among them argue strongly for coordinated and sustained observations, as well as additional careful modeling investigations in order to further advance the current understanding of the role of tropical oceans in climate.

Description: Coal-based power plants in India account for about 53% of installed capacity and contribute to more than 60% of electricity generation. Making use of data envelopment analysis and the Malmquist productivity index, this study analyses the productivity change of these plants during the period 2003–2008. The productivity change is further decomposed into technical efficiency change, technological change and scale efficiency change. The impact on productivity of capacity addition and scrapping obsolete power-generating modules is examined. It is found that the sector recorded an average annual total factor productivity (TFP) growth of 1.2% during the period. The plants located in eastern and southern regions have achieved maximum annual growth of 2.2% and 2.0%, respectively. Plants in the central sector achieved maximum growth of 4.2% annually. It is also found that the impact of capacity addition resulted in reduced TFP growth. This study identifies the plants whose productivity changes have been progressive and regressive; such identification can guide suitable managerial intervention to improve productivity.