Current Projects
Improved understanding of air-sea interaction processes and biases in the Tropical Western Pacific using observation sensitivity experiments and global forecast models [2019-2021] (PI, Funded by NOAA CVP)The project proposes to determine physical mechanisms governing air-sea interactions in the tropical west Pacific at the eastern edge of the warm pool by isolating coupled feedback processes through analyses of short-term coupled and uncoupled forecasts. Climate model forecasts of the Madden–Julian Oscillation (MJO) and El Niño–Southern Oscillation (ENSO) experience a systematic climate drift resulting in biases of the modeled tropical western Pacific climatology. Global models tend to have excess rainfall in the warm pool region and a deficiency in rainfall at the eastern edge of the warm pool. We propose to increase understanding of the dynamics and thermodynamics in the region by utilizing the Community Earth System Model (CESM) global runs as well as high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) regional uncoupled simulations.
The California Current upwelling system (CCS) supports one of the most productive marine ecosystems in the world and is a primary source of ecosystem services for the U.S. including fishing, shipping, and recreation. Despite the empirical evidence of ENSO influence upon the California Current marine ecosystems, the detailed influence of different ENSO events is unclear, and the degree of predictability of the various ecosystem drivers for specific tropical Pacific conditions has never been quantified. The goal of this project is to: 1) Use high-resolution ocean reanalysis of the CCS to link the physical drivers of the CCS ecosystem (temperature, upwelling velocity, alongshore & cross-shore transport) to local climate forcing functions (e.g. alongshore winds, wind stress curl, heat fluxes, precipitation and river runoff) at seasonal timescale; 2) Use long reanalysis products (e.g. SODAsi.3, 20CRv2c, CERA-20C) in combination with multiple linear regression and Singular Value Decomposition to objectively link the climate forcing functions variations in the CCS region with conditions (e.g. sea surface temperature, thermocline depth, sea surface height, tropical wind stresses) in the tropical Pacific that can optimally force them at seasonal timescales; and 3) Use Linear Inverse Modeling (LIM) and the North American Multi-Model Ensemble (NMME) to determine the predictability and uncertainty of the forcing functions along the CCS region, compare the LIM and NMME forecast skills, and explore possible sources of error in the NMME models.
The project goals are better understanding the ocean influence on the intensity and propagation speed (roughly 1 degree north per day) of the coupled ocean-atmosphere MISO signal. Determining how the large-scale upper ocean variability in the Northern Indian Ocean, which includes shallow salinity-driven mixed layers in the north and deeper mixed layers in the south, influences the MISO signal. Evaluating how the submesoscale and mesoscale perturbations and processes that govern the oceanic background state communicate with and influence the MISO. Integrate data and models to determine the spatial and temporal scales at which atmospheric and oceanic signatures need to be coupled to accurately capture the MISO propagation.
The project team proposes a coordinated research effort to better understand the basic physical dynamics of Pacific decadal variability and assess the skill of Pacific decadal predictability, along with its uncertainties and practical value. The research focuses on Community Earth System Model (CESM), with its vast repository of archived runs supplemented with targeted predictability experiments. The analysis focuses on using sophisticated statistical models (Linear Inverse Models) to identify statistical relations among variables, diagnose physical processes, and isolate potentially predictable components of the flows. It also involves using regional coupled atmosphere-ocean, along with uncoupled ocean and atmosphere models, to enhance the understanding of regional response and its potential for practical use in forecasting. The project brings together scientists skilled with developing decadal climate diagnostics, making both statistical and dynamical predictions, and executing regional coupled climate downscaling and regional high-resolution ocean modeling.
Completed Projects
We plan to study the spatiotemporal structures of bias development in CESM forecasts, launched from numerous initial states and during which random ENSO and MJO events occur, to determine the relative importance of poor mean-state representation versus the integrated impacts of the transient flows. This bias development will be studied as a function of the season to account for significant changes in the background state of the coupled ocean-atmosphere system in the tropical Pacific. We will also seek to ascribe these effects to well known physical processes for the specific climate modes of variability. We will test the sensitivity of the bias development to changes in coupled model resolution and model parameter selection. We will also implement nudging experiments (towards observations) to pinpoint where the worst parts of the biases develop apart from the nudged variables.
Assessing the Impact of Diurnal Wind Variability [2014 - 2018] (co-I, Funded by NASA)
The planned launch of RapidScat aboard the International Space Station, which does not follow a sun-synchronous orbit, will offer further opportunities to assess diurnal variability of ocean winds. The objective of this study will focus on first characterizing seasonal and interannual variability of diurnal winds both using multi-sensor measurements (from ASCAT, OSCAT, and WindSat) and also RapidScat measurements once they become available. This will allow us to characterize seasonal changes in the diurnal amplitude and year-to-year variations in the structure and magnitude of diurnal winds. Weather stations and mooring-based winds will be used to validate satellite-derived results. While satellite-based assessments of diurnal winds have typically been limited to examining a single diurnally varying harmonic, the more extensive sampling should make it possible to consider semi-diurnal and higher frequencies as well. The study will also use an one-dimensional upper-ocean model with high vertical resolution to quantify the role that diurnal winds play in upper-ocean processes. Results will be used to develop improvements for a more idealized model used to represent upper ocean processes in prognostic climate models. Our goals are to assess the impact of including or neglecting diurnally varying winds on large-scale ocean circulation studies and to quantify the impact of the rectified effects of radiative forcing and diurnal winds on surface temperature and salinity, upper ocean heat content and air-sea heat fluxes.
As a follow-up of this project, we developed a dataset for stochastic representation of sub-grid uncertainty for dynamical core development. More details on this sub-project including the data files and code to run the experiments can be found here.
Evaluating the Roles of Factors Critical to MJO Simulations Using the NCAR CAM3 with Deterministic and Stochastic Convection Parameterization Closures (Funded by NSF [2012 - 2014])
The Madden-Julian Oscillation (MJO) is a large-scale pattern of precipitation and atmospheric circulation anomalies which forms over the Indian Ocean and propagates slowly eastward towards the central equatorial Pacific Ocean. Impacts of the MJO are felt over much of the earth, and the presence of the MJO in the central equatorial Pacific Ocean exerts a strong modulation of hurricane activity in the Gulf of Mexico. Work conducted for this project is intended to improve the representation of the MJO in global climate models (GCMs), particularly the National Center for Atmospheric Research Community Atmosphere Model (CAM). This work was being done in collaboration with Dr. Guang Zhang and Dr. Mitch Moncrieff. The questions addressed in the research focus on specific factors believed to be essential to a successful MJO simulation: - How important is convective momentum transport [CMT, occurring in cumulus clouds] in the MJO simulation?
- The objective of addressing this question is to understand what role convective momentum transport plays in westerly wind bursts accompanying MJOs.
- What are the roles of convective versus stratiform heating, shallow versus deep convection, and stochastic versus deterministic convection closure in the MJO simulation?
- The objective is to determine how each of the processes affects the MJO simulation [here stratiform refers to the flat, upper-level "anvils" associated with deep cumulus clouds].
- How do these roles depend on the MJO phases in the MJO development process?
- The objective of answering this question is to understand what role each process plays during different stages of the MJO evolution. Motivation for the work comes from the difficulty in simulating and predicting the MJO using weather and climate models.
The ability of high resolution eddy-resolving ocean models to accurately resolve the South East Pacific eddy structure was critically tested using the VOCALS-REx (VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment) oceanographic mesoscale survey and satellite data. We are using ROMS (Regional Ocean Modeling System) to simulate the horizontal and vertical eddy structure. Synthetic analyses for the Regional Experiment using ocean data assimilation to understand the eddy heat/freshwater/tracer/nutrient transports from the coastal upwelling region to the remote SEP, and interactions between the eddies, the mixed layer and the deeper ocean. I am currently testing data assimilation twin experiments in Incremental Strong-constraint 4DVAR (IS4DVAR) framework of a high resolution (7 km horizontal resolution) regional ocean model of the South East Pacific region under the guidance of Prof. Art Miller and Prof. Bruce Cornuelle. I have run sample experiments to test the corrections in the ocean state estimation due to data assimilation in twin experiments. I plan to use ship cruise data from the VOCALS-REx funded cruise in Oct-08 to assimilate real time data from satellites and ship cruise surveys to better estimate the ocean state for the period during the ship cruise.
Publications from this project:
We are also learning and extending the application of optimal nonlinear filtering theory for ocean state estimation in collaboration with Prof. Ibrahim Hoteit, Prof. Bruce Cornuelle and Prof. Art Miller. We are testing different flavors of Ensemble Kalman Filters and also particle filters to use in simple nonlinear problems such as Lorenz-86 model and then extend the study to simple ocean models such as the barotropic vorticity model. We plan to work on the development, implementation and testing of several Particle Kalman Filters in simple test cases to analyze their behavior and understand their merits and demerits in data assimilation for real ocean and atmospheric models.
Publications from this project:
Subramanian et al. 2011
Coupled oceanic-atmospheric processes were analyzed using a regional coupled model ( SCOAR) in the Indian Ocean region to understand the controlling parameters on the Intraseasonal Oscillations of the Asian Summer Monsoon (ASM). This work is in collaboration with Prof. Art Miller, Prof. Raghu Murtugudde, Dr. Markus Jochum and Dr. Hyodae Seo. We are analyzing the influences of the air-sea fluxes and its variability on the intraseasonal variability of the ASM. Also, research interests diversify into the variability in the intraseasonal timescale in the internal ocean like the mixed layer and its predictability on the variability of the ASM at the intraseasonal timescale.
Publications from this project:
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