Recent Doctoral Defenses – Part I

Bohar Singh

Bohar Singh defended his dissertation in Fall 2017 (advisor: Jim Kinter) on “Seasonality of the Tropical Intraseasonal Oscillations (TISOs): Sensitivity to Mean Background State”. Singh came to the George Mason from the Indian Institute of Science in Bangalore, India.

TISO refers to variations in atmospheric convection (hence rainfall and other features) that occur over about 1-3 months. One example of TISO is the Madden-Julian Oscillation (MJO), a band of enhanced convection that drifts eastward along the equator. Another example is Monsoon Intra-Seasonal Oscillation (MISO), which is an increase or decrease in convection that propagates northward over India during the rainy summer monsoon. TISOs are not as well understood as mid-latitude weather, which causes variations over a few days, and El Nino-Southern Oscillation, which causes year-to-year variation.

Singh found that the co-occurrence of warm climatological SST and mean westerly wind plays an important role in setting the location and propagation direction of TISO. Sensitivity experiments with an atmospheric General Circulation Model indicate that the regionality and seasonality of TISO are closely coupled to local sea surface temperature (SST) and the low-level circulation. The SST in the tropics must reach a required threshold for convection to occur, while the low-level circulation controls the direction of propagation by controlling the location of moisture convergence.

Dr. Singh is now a postdoctoral fellow at Colorado State University.

Guangyang Fang

Guangyang Fang, working with Bohua Huang, defended his dissertation in Spring 2018. His research concerned “Seasonal Predictability of Tropical Atlantic Variability”.

Tropical Atlantic Variability (TAV) refers to year-to-year differences in sea surface temperature (SST) and related properties such as rainfall. So far, attempts to predict TAV months or more in advance
have not been very successful. This may be because TAV, unlike some other climate variability such as El Nino, is simply not predictable. On the other hand, climate models used to predict TAV have known flaws
which may be limiting predictability. If the latter is true, it is possible that improving the models will some day allow for better predictions.

Fang tested this idea with “perfect model” experiments, in which a climate state of the model itself is treated as “reality”. The model is then rerun starting at the climate state a few months earlier, but
with some random differences from the first run that represent small errors that are always present in measurements used to initialize the models. Since the same model, flaws and all, is used to create the “real” behavior and the “predicted” behavior, the flaws should not ruin the predictions. However, if the system is not predictable, then the model predictions will diverge from the original run just because
of the slightly different initial conditions.

Much of TAV is centered on either the northern hemisphere or the southern hemisphere of the tropical Atlantic. The experiment showed some prediction skill up to 9 months in advance for the northern mode and only 4 months for the southern mode. Fang’s diagnosis of the detailed behavior of the system showed that the northern mode was predictable because El Nino (which occurs primarily in the Pacific) influences the Atlantic, and the model shows skill in predicting El Nino. Predictability of the southern mode comes from the atmosphere-ocean interaction within the Atlantic region, and is not as robust as El Nino predictability.