Institute for Computational and Experimental Research in Mathematics (ICERM)

Abstract

In situ and satellite observations show that upper ocean temperature, salinity and currents often change hand-in-hand with monsoon variability, particularly on sub-seasonal time scales. In this talk, I present examples of coherent variations in the atmosphere and ocean.

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Abstract

Data assimilation (DA) shares many mathematical similarities with methods in artificial intelligence and machine learning (AI/ML). New methods are being developed to leverage these connections to improve capabilities in coupled data assimilation (CDA) for the initialization of coupled forecast models. Such coupled models are being used by operational prediction centers for forecast applications ranging from medium-range weather forecasts to subseasonal-to-seasonal (S2S) prediction. Advances by the integrated AI/ML & DA research group at CIRES and the NOAA Physical Sciences Laboratory will be discussed.

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Abstract

The variability of the Indian summer monsoon on timescales ranging from days to months to years isonly poorly simulated by existing models, pointing to the need to better understand the complex dynamics of this phenomenon. The underlying feedbacks or processes that drive this variability are not well understood. One of the key questions in this regard is whether or not the feedbacks are internal, i.e., specific to the region, or global. We use a simple dynamical model to investigate the main ingredients for capturing the qualitative aspects of the Indian monsoon and variability.

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Forecasting the southwest monsoon variability in the Bay of Bengal (BoB) requires coupled air-sea numerical models with an accurate representation of the atmosphere and ocean physics. Here we additionally explore the importance of biophysical interactions, where we consider how intraseasonal oscillations can modulate primary productivity, and how primary productivity could influence monsoonal variability. The cloudy southwest monsoon decreases light availability, inducing a temporary transition from nutrient- to light-limited productivity wherein subsurface irradiance and primary productivity co-vary, derived from diel cycles in co-located measurements of downwelling irradiance and subsurface chlorophyll-a fluorescence. This has implications for carbon fixation within the BoB, in addition to higher food chain impacts. As phytoplankton concentrations increase, light attenuation also increases, modifying the vertical distribution of solar heating over the upper water column. By employing one-dimensional air-sea numerical models forced with the observed atmospheric forcing, we investigate the sensitivity of sea surface temperature and mixed layer depth to the time- and depth-variable light attenuation. In summary, primary productivity, light attenuation, and sea surface temperature are potentially interdependent during the southwest monsoon, highlighting the need for realistic estimates of light attenuation when forecasting monsoon variability.

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Abstract

It is well-established that warm ocean currents play an essential role in the systematic development of extratropical cyclones. Since the Indian Ocean is landlocked on its northern side, one expects no such strong air-sea interaction. However, in this talk, I show how the SST fronts in the Bay of Bengal modulate Indian summer monsoon rainfall over India at different temporal scales. We have investigated how the SST fronts and gradients in the Bay of Bengal modulate the monsoon rainfall at different time scales by conducting well- designed model experiments. A narrow coastal Bay of Bengal SST front is essential to sustain rain over central India. The SST front promotes intense convection in its vicinity to source the monsoon-low pressure systems. Basin- wide meridional SST gradients support intraseasonal monsoon rainfall over India. Small scale SST eddies in the Bay of Bengal also appears to be modulating monsoon rainfall at a seasonal time scale.

Abstract

Observability is a well defined notion in the theory of controls for dynamical systems which, in a sense is dual to the idea of controllability. In particular observability plays an important role in data assimilation. After a short review of the basic ideas, I'll introduce a range of recent applications to partial differential equations arising in geophysics. I'll also present some recent work on image-based observers and other similar dynamically interesting quantities, which can also be used to infer the state of the system. The talk is aimed at a general scientific audience and will not assume more than a familiarity with differential equations.

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Abstract

The ocean and atmosphere communicate with one another through exchanges of heat, moisture, momentum, and gases across the across the air-sea interface. These surface exchanges can regulate the multi-scale processes within the Asian monsoon system. In this talk, I will give an overview of tropical ocean-atmosphere coupled feedbacks as they relate to the South Asian Monsoon system, and how biases in the representation of these feedbacks in coupled forecast models may contribute monsoon prediction skill.

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Abstract

El Niño–Southern Oscillation (ENSO) exerts a strong positive influence on the precipitation variability over Central Southwest Asian (CSWA) region during the wet season that spans from November to April. We note that the ENSO influence varies intra-seasonally and has two components, one directly through the equatorial Pacific region and one indirectly through the tropical Indian Ocean. In both cases, ENSO exerts its influence through Rossby wave-like atmospheric anomalies. When the two components are in phase, ENSO has the strongest influence while it is weakest when they are out of phase. These findings suggest that improvements in sub-seasonal to seasonal scale predictability requires the better representation of intra-seasonal variability of ENSO teleconnection, as well as the role of inter-basin interactions in its propagation.

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Abstract

In the past four decades, several studies have contributed to improving our theoretical understanding of the tropical intraseasonal oscillation (TISO) over the Indian Ocean region.
Improved theoretical understanding can help improve our models and their predictions of these disturbances. Progress in modeling and predicting the ISO will has been achieved by understanding the underlying complex feedbacks and interactions amongst the earth system components during a monsoon intraseasonal oscillation. Significant progress in theoretical understanding has been achieved, yet some aspects of the theories remain incomplete. Here we will present some of the existing theories that explain the generation and maintenance of the monsoon intraseasonal oscillation. We will then discuss how stochastic modeling in earth system prediction can help improve forecasts of intraseasonal oscillations over the Indian Ocean region. Coupled air-sea interaction processes relevant to intraseasonal variability (e.g. the MJO, MISO) in the earth’s climate system are inadequately represented in regional and global coupled models. These inaccuracies could be related to the either poor parameterization of model physics or insufficient model resolution to resolve the critical processes. In this presentation, we will present forecast evaluations of a series of medium and subseasonal-range hindcasts to show that stochastic modeling approaches have a significant impact on tropical intraseasonal forecast skill and the statistics of precipitation generated by these storms. This has implications on improving conventional convection parameterization for predicting such high impact weather events as we await the exascale computing systems of the future to resolve convective processes in weather models.

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Recent modelling work suggests that the monsoon is sensitive to SST and its gradients in an approximately 100 km band along the north coast of the Bay of Bengal. I call this region the Head Bay Warm Zone (HWZ). Here, I review the oceanography of the HWZ and suggest ways to improve predictions. Surprisingly, the HWZ, particularly the region of the Ganges delta shelf is poorly studied. The mixed layer depth in the HWZ is shallower than in the central Bay, often less than 10 meters, due to the lower salinity from river water input. During the monsoon the net heat flux warms the ocean so the HWZ is warmer than the central Bay on average. However, the fluctuations in this gradient are large due to variations in the river input and the MISO cycle. In particular, the salinity of the HWZ increases dramatically during the monsoon so air-sea interactions may be quite different in the early and late monsoons. The importance of these fluctuations is not well understood. Upwelling along the coast does not cool the HWZ because the upwelled water has the same temperature as the surface water due to mixing during the early monsoon. Model simulations often use climatological estimates of the mixed layer depth. These may differ significantly from the thermodynamic mixed layer which is the best measure for air-sea interaction. Similarly, mixing parameterizations for very shallow mixed layers may have systematic errors. Finally, if salinity is important to monsoon dynamics, then the persistence of salinity in the Bay of Bengal over multiple years may introduce some memory into this system.

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