Subramanian Aneesh C.

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Subramanian
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Aneesh C.
ORCID
0000-0001-7805-0102

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  • Article
    The role of air-sea interactions in atmospheric rivers: Case studies using the SKRIPS regional coupled model
    (American Geophysical Union, 2021-02-12) Sun, Rui ; Subramanian, Aneesh C. ; Cornuelle, Bruce D. ; Mazloff, Matthew R. ; Miller, Arthur J. ; Ralph, F. Martin
    Atmospheric rivers (ARs) play a key role in California's water supply and are responsible for most of the extreme precipitation and major flooding along the west coast of North America. Given the high societal impact, it is critical to improve our understanding and prediction of ARs. This study uses a regional coupled ocean–atmosphere modeling system to make hindcasts of ARs up to 14 days. Two groups of coupled runs are highlighted in the comparison: (1) ARs occurring during times with strong sea surface temperature (SST) cooling and (2) ARs occurring during times with weak SST cooling. During the events with strong SST cooling, the coupled model simulates strong upward air–sea heat fluxes associated with ARs; on the other hand, when the SST cooling is weak, the coupled model simulates downward air–sea heat fluxes in the AR region. Validation data shows that the coupled model skillfully reproduces the evolving SST, as well as the surface turbulent heat transfers between the ocean and atmosphere. The roles of air–sea interactions in AR events are investigated by comparing coupled model hindcasts to hindcasts made using persistent SST. To evaluate the influence of the ocean on ARs we analyze two representative variables of AR intensity, the vertically integrated water vapor (IWV) and integrated vapor transport (IVT). During strong SST cooling AR events the simulated IWV is improved by about 12% in the coupled run at lead times greater than one week. For IVT, which is about twice more variable, the improvement in the coupled run is about 5%.
  • Article
    Observational needs supporting marine ecosystems modeling and forecasting: from the global ocean to regional and coastal systems
    (Frontiers Media, 2019-10-15) Capotondi, Antonietta ; Jacox, Michael ; Bowler, Chris ; Kavanaugh, Maria T. ; Lehodey, Patrick ; Barrie, Daniel ; Brodie, Stephanie ; Chaffron, Samuel ; Cheng, Wei ; Dias, Daniela F. ; Eveillard, Damien ; Guidi, Lionel ; Iudicone, Daniele ; Lovenduski, Nicole S. ; Nye, Janet A. ; Ortiz, Ivonne ; Pirhalla, Douglas ; Pozo Buil, Mercedes ; Saba, Vincent S. ; Sheridan, Scott ; Siedlecki, Samantha A. ; Subramanian, Aneesh C. ; de Vargas, Colomban ; Di Lorenzo, Emanuele ; Doney, Scott C. ; Hermann, Albert J. ; Joyce, Terrence M. ; Merrifield, Mark ; Miller, Arthur J. ; Not, Fabrice ; Pesant, Stephane
    Many coastal areas host rich marine ecosystems and are also centers of economic activities, including fishing, shipping and recreation. Due to the socioeconomic and ecological importance of these areas, predicting relevant indicators of the ecosystem state on sub-seasonal to interannual timescales is gaining increasing attention. Depending on the application, forecasts may be sought for variables and indicators spanning physics (e.g., sea level, temperature, currents), chemistry (e.g., nutrients, oxygen, pH), and biology (from viruses to top predators). Many components of the marine ecosystem are known to be influenced by leading modes of climate variability, which provide a physical basis for predictability. However, prediction capabilities remain limited by the lack of a clear understanding of the physical and biological processes involved, as well as by insufficient observations for forecast initialization and verification. The situation is further complicated by the influence of climate change on ocean conditions along coastal areas, including sea level rise, increased stratification, and shoaling of oxygen minimum zones. Observations are thus vital to all aspects of marine forecasting: statistical and/or dynamical model development, forecast initialization, and forecast validation, each of which has different observational requirements, which may be also specific to the study region. Here, we use examples from United States (U.S.) coastal applications to identify and describe the key requirements for an observational network that is needed to facilitate improved process understanding, as well as for sustaining operational ecosystem forecasting. We also describe new holistic observational approaches, e.g., approaches based on acoustics, inspired by Tara Oceans or by landscape ecology, which have the potential to support and expand ecosystem modeling and forecasting activities by bridging global and local observations.
  • Article
    Coupled impacts of the diurnal cycle of sea surface temperature on the Madden–Julian oscillation
    (American Meteorological Society, 2014-11-15) Seo, Hyodae ; Subramanian, Aneesh C. ; Miller, Arthur J. ; Cavanaugh, Nicholas R.
    This study quantifies, from a systematic set of regional ocean–atmosphere coupled model simulations employing various coupling intervals, the effect of subdaily sea surface temperature (SST) variability on the onset and intensity of Madden–Julian oscillation (MJO) convection in the Indian Ocean. The primary effect of diurnal SST variation (dSST) is to raise time-mean SST and latent heat flux (LH) prior to deep convection. Diurnal SST variation also strengthens the diurnal moistening of the troposphere by collocating the diurnal peak in LH with those of SST. Both effects enhance the convection such that the total precipitation amount scales quasi-linearly with preconvection dSST and time-mean SST. A column-integrated moist static energy (MSE) budget analysis confirms the critical role of diurnal SST variability in the buildup of column MSE and the strength of MJO convection via stronger time-mean LH and diurnal moistening. Two complementary atmosphere-only simulations further elucidate the role of SST conditions in the predictive skill of MJO. The atmospheric model forced with the persistent initial SST, lacking enhanced preconvection warming and moistening, produces a weaker and delayed convection than the diurnally coupled run. The atmospheric model with prescribed daily-mean SST from the coupled run, while eliminating the delayed peak, continues to exhibit weaker convection due to the lack of strong moistening on a diurnal basis. The fact that time-evolving SST with a diurnal cycle strongly influences the onset and intensity of MJO convection is consistent with previous studies that identified an improved representation of diurnal SST as a potential source of MJO predictability.
  • Article
    Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments
    (Elsevier, 2020-02-20) Jacox, Michael ; Alexander, Michael A. ; Siedlecki, Samantha A. ; Chen, Ke ; Kwon, Young-Oh ; Brodie, Stephanie ; Ortiz, Ivonne ; Tommasi, Desiree ; Widlansky, Matthew J. ; Barrie, Daniel ; Capotondi, Antonietta ; Cheng, Wei ; Di Lorenzo, Emanuele ; Edwards, Christopher ; Fiechter, Jerome ; Fratantoni, Paula S. ; Hazen, Elliott L. ; Hermann, Albert J. ; Kumar, Arun ; Miller, Arthur J. ; Pirhalla, Douglas ; Pozo Buil, Mercedes ; Ray, Sulagna ; Sheridan, Scott ; Subramanian, Aneesh C. ; Thompson, Philip ; Thorne, Lesley ; Annamalai, Hariharasubramanian ; Aydin, Kerim ; Bograd, Steven ; Griffis, Roger B. ; Kearney, Kelly ; Kim, Hyemi ; Mariotti, Annarita ; Merrifield, Mark ; Rykaczewski, Ryan R.
    Marine ecosystem forecasting is an area of active research and rapid development. Promise has been shown for skillful prediction of physical, biogeochemical, and ecological variables on a range of timescales, suggesting potential for forecasts to aid in the management of living marine resources and coastal communities. However, the mechanisms underlying forecast skill in marine ecosystems are often poorly understood, and many forecasts, especially for biological variables, rely on empirical statistical relationships developed from historical observations. Here, we review statistical and dynamical marine ecosystem forecasting methods and highlight examples of their application along U.S. coastlines for seasonal-to-interannual (1–24 month) prediction of properties ranging from coastal sea level to marine top predator distributions. We then describe known mechanisms governing marine ecosystem predictability and how they have been used in forecasts to date. These mechanisms include physical atmospheric and oceanic processes, biogeochemical and ecological responses to physical forcing, and intrinsic characteristics of species themselves. In reviewing the state of the knowledge on forecasting techniques and mechanisms underlying marine ecosystem predictability, we aim to facilitate forecast development and uptake by (i) identifying methods and processes that can be exploited for development of skillful regional forecasts, (ii) informing priorities for forecast development and verification, and (iii) improving understanding of conditional forecast skill (i.e., a priori knowledge of whether a forecast is likely to be skillful). While we focus primarily on coastal marine ecosystems surrounding North America (and the U.S. in particular), we detail forecast methods, physical and biological mechanisms, and priority developments that are globally relevant.