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

Upper ocean processes play a key role in air-sea coupling, with variability on both short and long time scales. The diurnal cycle associated with diurnal solar insolation and nighttime cooling, may act, along with stochastic wind variability, on upper ocean temperatures and stratification resulting in a diurnal warm layer and a nonlinear rectified effect on longer time scales. This study describes diurnal changes in upper ocean temperature for a location in the equatorial Indian Ocean, using observations from the Dynamics of the Madden-Julian Oscillation field campaign, a high vertical resolution 1-D process model, and a diurnal cycling scheme. Solar forcing is the main driver of diurnal variability in upper ocean temperature and stratification. Yet except during nighttime convection, winds with variability on the order of hours (here referred to as “wind gusts”) regulate how fast surface water is mixed to greater depths when daily mean winds are weak. Wind gusts are much stronger than diurnal winds. Even using stochastic wind gusts but no diurnal winds as input in a 1-D process model yields an estimate of diurnal temperature that compares well with observations. A new version of the Large and Caron (2015) scheme (LC2015) provides an estimate of upper ocean diurnal temperature that is consistent with observations. LC2015 has the advantage of being suitable for implementation in a climate model, with the goal to improve SST estimates, hence the simulated heat flux at the air-sea interface. Yet LC2015 is not very sensitive to the inclusion or omission of the high-frequency component of the wind.

Projections of future climate are sensitive to the representation of upper-ocean diurnal variability, including the diurnal cycle of winds. Two different methods suitable for time series with missing data are used here to characterize how observed diurnal winds vary over the year. One is based on diurnal composites of mooring data, and the other is based on harmonic analysis via a least squares fit and is able to isolate annual (i.e., 1 cycle per year) modulation of diurnal variability. Results show that the diurnal amplitude in meridional winds is larger than in zonal winds and peaks in the tropical Pacific, where diurnal variability in zonal winds is overall weaker compared to other basins. Furthermore, the amplitude and phasing of diurnal winds in the tropical oceans are not uniform in time, with overall larger differences through the year in the meridional component of tropical winds. Estimating the annual modulation of the diurnal signal implies resolving both the diurnal energy peak and also the modulation of this peak by the annual cycle. This leads to a recommendation for sampling at least 6 times per day and for a duration of at least 3 years.

Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the MERRA-2 reanalysis from January 1980 to June of 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month proof of concept trial run designed to illustrate the utility and feasibility of the ARTMIP project.

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month proof-of-concept trial run designed to illustrate the utility and feasibility of the ARTMIP project.