Modeling efforts to improve the Asian Summer Monsoon representation in a coupled ocean-atmosphere tropical-channel model

Abstract:

The Asian Summer Monsoon (ASM) simulated over the 1989-2009 period with a new 0.75° coupled ocean-atmosphere tropical-channel (45°S-45°N) model based on WRF and NEMO models is presented. The model biases are comparable to those commonly found in coupled global coupled models (CGCMs): the Findlater jet is too weak, precipitations are underestimated over India while they are overestimated over South-East Asia and the Maritime Continent. The ASM onset is delayed by several weeks, an error which is also very common in current CGCMs.

We show that land surface temperature errors are a major source of the ASM low-level circulation and rainfall biases in our model: a cold bias over the Middle-East region weakens the Findlater jet while a warm bias over India strengthens the monsoon circulation in the Bay of Bengal. To explore the origins of those biases and their relationship with the ASM, a series of sensitivity experiments is presented.

First, we show that changing the land surface albedo representation in our model directly influences the ASM characteristics by reducing the cold bias in the Middle-East region. It improves the “heat low” representation, which has direct implication on the Findlater jet strength and precipitation over India. Furthermore, the ASM onset is shifted back by almost one month in agreement with observations.

Second, a parameterization of the convective cloud-radiative feedback is introduced in the atmospheric model. It acts to reduce the warm bias present in convective regions such as India and favors the monsoon northward migration. As a consequence, the dry bias is reduced in this region.

Finally, horizontal resolution is increased from 0.75° to 0.25° for both oceanic and atmospheric models to assess the sensitivity of the ASM biases to the model resolution. Large-scale model errors persist at higher resolution, but are significantly attenuated. Precipitation is improved in mountainous areas with strong orographic control, but also in other regions where orography is weak such as central India.