Current ProjectsMesoscale Drivers of Oxygen in the Tropical Pacific [2020-2023] (NSF)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. 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. 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. Improved understanding of air-sea interaction processes and biases in the Tropical Western Pacific using observation sensitivity experiments and global forecast models [2019-2021] (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. Understanding and Quantifying the Predictability of Marine Ecosystem Drivers in the California Current System [2018-2021] (NOAA MAPP)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. MISO-BOB: Prediction of Monsoon Intra-Seasonal Oscillations Using High-Resolution Coupled Modeling and Data Assimilation [2018 - 2022] (ONR)
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 ProjectsEASM-3: Quantifying Predictability Limits, Uncertainties, Mechanisms, and Regional Impacts of Pacific Decadal Climate Variability [2014 - 2020] (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. Towards the Prototype Probabilistic Earth-System Model for Climate Prediction [2014 - 2017] (Funded by ERC) 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:
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: Subramanian et al. 2011 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: Subramanian et al. 2013 I maintain my research pages with bibliography at these pages. Also, my calendar can be found at this page. |