Sources of Errors in the Simulation of South Asian Summer Monsoon in the CMIP5 GCMs
Scientists investigate the systematic errors in simulating the South Asian summer monsoon rainfall across global climate models.
Accurately simulating the characteristics of the South Asian Monsoon (SAM) through space and time, and the mechanisms that drive it, is a significant challenge. Most global climate models (GCMs) are largely unsuccessful in simulating fundamental aspects of the monsoon. Analysis of a multi-model ensemble of GCM simulations highlighted how differences in land-atmosphere interactions represented in GCMs lead to differences in simulating the development and maintenance of the SAM.
A study performed by researchers including a scientist at the U.S. Department of Energy’s (DOE) Pacific Northwest National Laboratory (PNNL) identified improvements in representing land-atmosphere processes as a key step toward improving GCM simulations of the South Asian summer monsoon rainfall. This has significant implications for the well-being and economic security of the South Asian population, as GCMs are essential tools for subseasonal-to-seasonal monsoon prediction and projecting monsoon changes in the future.
Accurate simulation of the South Asian summer monsoon remains a formidable challenge, but no effort has yet deciphered the origin of GCM biases over the region. Analysis of a large ensemble of GCMs showed that most of the simulation errors in the SAM rainfall were of similar nature across the models. Biases in the land-sea heating contrast played a critical role in determining the onset timing of the monsoon, the seasonal precipitation distribution, and the trajectories of monsoon depressions. Most of the summer monsoon errors were reproducible in GCMs with prescribed sea surface temperatures, suggesting that the ocean did not play a key role in the model biases. Further analysis showed that the errors in the simulation of pre-monsoon land-atmosphere processes over the South Asian landmass initiated the summer monsoon biases. These results highlight the importance of previously less well-known pre-monsoon mechanisms that critically influence the strength of SAMs in the GCMs, and of land-atmosphere interactions in the development and maintenance of SAMs.
Pacific Northwest National Laboratory
- Regional & Global Climate Modeling
- Water Cycle and Climate Extremes Modeling