[1] There is an established evidence of climatic teleconnection between El Niño– Southern Oscillation (ENSO) and Indian summer monsoon rainfall (ISMR) during June through September. Against the long-recognized negative correlation between ISMR and ENSO, unusual experiences of some recent years motivate the search for some other causal climatic variable, influencing the rainfall over the Indian subcontinent. Influence of recently identified Equatorial Indian Ocean Oscillation (EQUINOO, atmospheric part of Indian Ocean Dipole mode) is being investigated in this regard. However, the dynamic nature of cause-effect relationship burdens a robust and consistent prediction. In this study, (1) a Bayesian dynamic linear model (BDLM) is proposed to capture the dynamic relationship between large-scale circulation indices and monthly variation of ISMR and (2) EQUINOO is used along with ENSO information to establish their concurrent effect on monthly variation of ISMR. This large-scale circulation information is used in the form of corresponding indices as exogenous input to BDLM, to predict the monthly ISMR. It is shown that the Indian monthly rainfall can be modeled in a better way using these two climatic variables concurrently (correlation coefficient between observed and predicted rainfall is 0.82), especially in those years when negative correlation between ENSO and ISMR is not well reflected (i.e., 1997, 2002, etc.). Apart from the efficacy of capturing the dynamic relationship by BDLM, this study further establishes that monthly variation of ISMR is influenced by the concurrent effects of ENSO and EQUINOO. Citation: Maity, R., and D. Nagesh Kumar (2006), Bayesian dynamic modeling for monthly Indian summer monsoon rainfall using El Niño–Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO), J. Geophys. Res., 111, D07104, doi:10.1029/2005JD006539. 1.

Using observation-driven simulations of global terrestrial hydrology and a cluster algorithm that searches for spatially connected regions of soil moisture, the authors identified 296 large-scale drought events (greater than 500 000 km 2 and longer than 3 months) globally for 1950–2000. The drought events were subjected to a severity–area–duration (SAD) analysis to identify and characterize the most severe events for each continent and globally at various durations and spatial extents. An analysis of the variation of large-scale drought with SSTs revealed connections at interannual and possibly decadal time scales. Three metrics of large-scale drought (global average soil moisture, contiguous area in drought, and number of drought events shorter than 2 years) are shown to covary with ENSO SST anomalies. At longer time scales, the number of 12-month and longer duration droughts follows the smoothed variation in northern Pacific and Atlantic SSTs. Globally, the mid-1950s showed the highest drought activity and the mid-1970s to mid-1980s the lowest activity. This physically based and probabilistic approach confirms well-known droughts, such as the 1980s in the Sahel region of Africa, but also reveals many severe droughts (e.g., at high latitudes and early in the time period)