Research Projects
Current Projects
The tropical Pacific is home to rich marine biodiversity and abundant fisheries. It is also the most oxygen-deficient basin in the world, hosting the world’s largest oxygen minimum zones (OMZs). These OMZs are separated by an equatorial oxygenated tongue that provides important habitable space for pelagic fisheries whose foraging behavior is limited by hypoxic depth. Characterizing processes modulating this hypoxic depth is critical to understanding ecosystem dynamics and predicting and managing fisheries in this region. The primary goal for this project is to gain a deeper understanding of mesoscale processes governing the 3-dimensional structure and seasonal- to-interannual variability of oxygen in the equatorial Pacific. A major knowledge gap concerns the role of the equatorial current system (ECS) and tropical instability vortices (TIVs) in ventilating the OMZs, and the extent to which oxygen supply by these ventilation pathways is compensated by their effects on nutrient transport, productivity, and respiration rates. A central hypothesis for this proposal is that lateral transport by the Equatorial Undercurrent and TIV-mediated fluxes play a dominant role in setting the mean O2 structure and variability of the upper equatorial Pacific. These questions are examined using a hierarchy of models of various configurations, including an eddy-resolving and coarse global model, an eddy-resolving data-assimilating regional model of the tropical Pacific, and Lagrangian analysis.
Atmospheric River Program Phase II [2020-2022] (CA DWR)
Heavy precipitation and floods in winter along the US west coast are largely caused by intense water vapor transport (integrated vapor transport (IVT)) within meteorological features known as atmospheric rivers (ARs). Previous studies have shown that forecasts of IVT have higher predictability than precipitation. The issuance of flood and heavy precipitation warnings in the US West Coast region by weather forecasters relies heavily on the model forecasts of AR activity. On the US East Coast and Gulf of Mexico coastline, the forecast reliability of the tropical cyclone intensity and landfall location have been closely studied and evaluated extensively. Similar progress on monitoring and quantitative evaluation of forecasts for IVT and ARs making landfall on the US West Coast is yet to be made. Dynamical models have relatively poor skill in forecasting ARs and high values of IVT beyond the weather timescales of a few days (Wick et al., 2013) although they are improving with better physics and higher resolutions. Given their critical role in the global water cycle and extreme precipitation events, it is important to improve our abilities to forecast IVT and ARs with the best tools available to us. Hence, we choose to combine statistical and dynamical approaches to improve the forecasts of IVT over the East Pacific Ocean and US West Coast region. We will then work to visualize these forecasts in a way that is intuitive to users by the end of the project.
Forecast Informed Reservoir Operations [2020-2025] (USACE)
This project proposes to develop methods of analysis and verification metrics for the intercomparison of global model forecasts for atmospheric rivers that make landfall over the Western US region. Global model forecasts from three international operational forecasting centers (National Center for Environmental Prediction (NCEP), European Center for Medium-range Weather Forecasts (ECMWF) and Naval Research Laboratory (NRL)) which assimilate dropsonde observations from the Intensive Observation Periods (IOPs) of Atmospheric River Reconnaissance 2018 and 2019 will be performed and compared. The three operational centers will also perform data denial experiments where observations from the AR Recon field campaign will be withheld and the rest of the observations during the IOPs will be assimilated into the model fields. Model forecasts will be performed from these data denial experiments. Analysis and quantification of the impact of the dropsondes on the forecasts of ARs that make landfall on the US West Coast and especially the watershed regions and regions of reservoirs of interest to the Forecast Informed Reservoir Operations (FIRO) program will be performed.
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.
Completed Projects
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.
Seasonal to Sub-seasonal (S2S) predictability of Heat Waves over the Western US: Impacts on Snowpack [2019-2021] (USBR)
This project undertakes an assessment of spring heat waves toward enhanced predictability at the seasonal to sub-seasonal timescale (~3-12 week lead times). Much of the research on heat waves has been focused on the summer season when already hot temperatures are exacerbated, producing conditions with significant impacts to public health, transportation, and other sectors. However, from a western water management perspective, specifically management of snowmelt dominated basins, spring heat waves are especially impactful for timing and other run-off characteristics. This work seeks to provide advanced forecasts of these spring heat waves in support of water management.
EASM-3: Quantifying Predictability Limits, Uncertainties, Mechanisms, and Regional Impacts of Pacific Decadal Climate Variability [2014 - 2018] (co-PI, Funded by NSF)
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.
A Nudging and Ensemble Forecasting Approach to Identify and Correct Tropical Pacific Bias-Producing Processes in CESM [2014 - 2018] (PI, Funded by NOAA)
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.
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.
Towards the Prototype Probabilistic Earth-System Model for Climate Prediction [2014 - 2017] (Funded by ERC)
This programme of research is intended to make climate simulations more consistent both with the multi-scale nature of climate, and with related scaling symmetries of the partial differential equations which govern climate. This will be achieved by moving away from the traditional deterministic approach to the closure problem in computational fluid dynamics, and towards a more novel description of physical processes near and below the truncation scale of climate models, using contemporary nonlinear stochastic-dynamic mathematics. The aim of the proposed research is to produce the world's first Probabilistic Earth System Model. The consequences are enormous: a comprehensive climate model with reduced biases against observations, a model which will be capable of producing estimates of uncertainty in its own predictions, and a model which can make use of emerging energy-efficient probabilistic processor hardware, key to practical success as we approach the era of the exascale supercomputer. The development of the prototype Probabilistic Earth-System Model will open a new era of international scientific collaboration on climate model development, and has the potential to influence climate policy, on mitigation, adaptation and on geoengineering, at the highest governmental and intergovernmental levels.
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.
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.
Simulation and data assimilation of South East Pacific (SEP) ocean mesoscale structure (Funded by NSF [2008 - 2012])
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:
Inverse Methods for Data assimilation in nonlinear problems (Funded by ONR [2008-2010])
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:
Coupled Ocean-Atmospheric processes in MJO-ENSO Dynamics (Funded by ONR [2009 - 2013])
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:
I maintain my research pages with bibliography at these pages. Also, my calendar can be found at this page.