Paper Reviewed
Singh, U.K., Singh, G.P. and Singh, V. 2015. Simulation skill of APCC set of global climate models for Asian summer monsoon rainfall variability. Theoretical and Applied Climatology 130: 109-122.

Working at institutions located in four different countries (Belgium, India, South Korea and the United Kingdom), Singh et al. (2015) assessed the skills of 11 Asia-Pacific Economic Cooperation Climate Center (APCC) global climate models (both coupled and uncoupled) in simulating the seasonal summer (June-August) monsoon rainfall variability over Asia (especially over India and East Asia) based on analyses of hind-cast data (3 months advance) generated from APCC models that provide regional climate product services based on "multi-model ensemble dynamical seasonal prediction systems." And what did that assessment reveal?

When all was said and done, Singh et al. report that (1,2) "the majority of the models have negative bias over some parts of the Asian land mass and over equatorial zones (Indian and west Pacific Oceans)," that (3,4) "large variability in simulated Asian summer monsoon rainfall was found from model to model and from region to region in uncoupled models compared to coupled models," that (5) "most of the models underestimated the rainfall over high rainfall belts," that (6) "atmospheric chaotic dynamics uncertainties in the representation of unresolved sub-grid scales in the models may cause large bias in the models," that (7) there were "large spreads in individual members of the model," and that (8) "these spreads were as large as the spread of ensemble means of different models," that there was (9) "a large variation in producing inter-annual variability especially over Indian and west Pacific Oceans," potentially due to (10) "poor representation of air-sea interaction," and that (11) "the exact amount of simulated summer monsoon rainfall in the multi-model ensemble over Asia is not fully captured."

And, therefore, in light of their many negative findings, Singh et al. conclude "there is a need to fully understand the physical processes used for improving the individual model skills rather than the methods of multi-model ensemble for producing better seasonal rainfall prediction systems."