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Multiscale Dynamics of Atmospheric and Oceanic Variability in the Climate System

Posted on:2013-07-16Degree:Ph.DType:Thesis
University:University of California, San DiegoCandidate:Subramanian, Aneesh CFull Text:PDF
GTID:2450390008985850Subject:Climate change
Abstract/Summary:
Multiscale dynamics in the atmosphere-ocean system is ubiquitous from dust devils in the garden to tropical cyclones to mesoscale eddies in the ocean and deep ocean circulation. The purpose of this thesis is to advance ocean-atmosphere general circulation modeling and methodologies for data assimilation in order to resolve more pieces of the climate modeling puzzle than before. Specifically, it focuses on modeling the Madden-Julian Oscillation in a climate model and modeling mesoscale eddies in a regional ocean model with data assimilation. While most climate models today fail to simulate even the large-scale features of the MJO, we find that CCSM4 reproduces many realistic aspects of MJO behavior. CCSM4 produces coherent, broadbanded and energetic patterns in eastward propagating intraseasonal zonal winds and OLR in the tropics with MJO-like characteristics. Broadscale patterns, as revealed in combined EOFs of U850, U200 and OLR, are remarkably consistent with observations and indicate that convective coupling occurs in the simulated MJOs. Relations between the MJO in the model and its concurrence with other climate states reveal that higher amplitude MJO activity occurs during weak warm ENSO events and during periods of weak meridional shear and negative zonal shear during the Indian Monsoons. MJO response to anthropogenic climate change is assessed using CCSM4 in a second study. In global warming scenarios, the model simulation indicates an increase in the number of days with higher amplitude MJOs and also a higher number of active MJO days in the Indian and West Pacific Ocean. These findings are consistent with MJO activity observations of the present and past.;The second half of this thesis discusses the implementation of assimilation methodologies in physical oceanography. It presents the mathematical recipe of the assimilation method adapted in the present study and also illustrates the implementation of assimilation of observed data over the oceans in a regional ocean model (ROMS) in the Southeast Pacific region. Fifteen-day ROMS I4D-VAR data assimilation fits are performed successfully. The normalized absolute misfit between the observations and the corresponding model states is reduced close to the observational error range. Eddies are identified from the ocean state estimate along the track of the VOCALS-Rex cruise of 2008 and studied for their hydrography and velocity structure. Cyclonic eddies (sea level anomaly lows) are characterized by shoaling isopycnals in the upper 300 m, colder temperature cores and a shallower salinity minima. It is also observed that the water mass properties of the core of the eddy has similar properties to that in the subantarctic water close to the coast. A heat budget analysis for the period of the cruise reveals that the advection is the predominant process that balances the surface heat flux and temperature tendency. Vertical diffusion is the second highest term balancing the heat budget with the horizontal diffusion being an order of magnitude smaller.;Lastly, the role of the linear analysis step of the ensemble Kalman filters (EnKF) in disrupting the balanced dynamics in a simple atmospheric model is investigated and compared to a fully nonlinear particle-based filter (PF). Identical twin experiments show that EnKF and PF capture the variables on the slow manifold well as the dynamics is very stable. PFs, especially the SKRF, capture slaved modes better than the EnKF implying a full Bayesian analysis estimates the nonlinear model variables better. The PFs perform significantly better in the fully coupled nonlinear model where fast and slow variables modulate each other. This suggests that the analysis step in the PFs maintains the balance in both variables much better than the EnKF.
Keywords/Search Tags:Ocean, Dynamics, Climate, MJO, Model, Enkf, Eddies, Variables
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