Kumar Arun

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  • Article
    Ocean observations to improve our understanding, modeling, and forecasting of subseasonal-to-seasonal variability
    (Frontiers Media, 2019-08-08) Subramanian, Aneesh C. ; Balmaseda, Magdalena A. ; Centurioni, Luca R. ; Chattopadhyay, Rajib ; Cornuelle, Bruce D. ; DeMott, Charlotte ; Flatau, Maria ; Fujii, Yosuke ; Giglio, Donata ; Gille, Sarah T. ; Hamill, Thomas M. ; Hendon, Harry ; Hoteit, Ibrahim ; Kumar, Arun ; Lee, Jae-Hak ; Lucas, Andrew J. ; Mahadevan, Amala ; Matsueda, Mio ; Nam, SungHyun ; Paturi, Shastri ; Penny, Stephen G. ; Rydbeck, Adam ; Sun, Rui ; Takaya, Yuhei ; Tandon, Amit ; Todd, Robert E. ; Vitart, Frederic ; Yuan, Dongliang ; Zhang, Chidong
    Subseasonal-to-seasonal (S2S) forecasts have the potential to provide advance information about weather and climate events. The high heat capacity of water means that the subsurface ocean stores and re-releases heat (and other properties) and is an important source of information for S2S forecasts. However, the subsurface ocean is challenging to observe, because it cannot be measured by satellite. Subsurface ocean observing systems relevant for understanding, modeling, and forecasting on S2S timescales will continue to evolve with the improvement in technological capabilities. The community must focus on designing and implementing low-cost, high-value surface and subsurface ocean observations, and developing forecasting system capable of extracting their observation potential in forecast applications. S2S forecasts will benefit significantly from higher spatio-temporal resolution data in regions that are sources of predictability on these timescales (coastal, tropical, and polar regions). While ENSO has been a driving force for the design of the current observing system, the subseasonal time scales present new observational requirements. Advanced observation technologies such as autonomous surface and subsurface profiling devices as well as satellites that observe the ocean-atmosphere interface simultaneously can lead to breakthroughs in coupled data assimilation (CDA) and coupled initialization for S2S forecasts. These observational platforms should also be tested and evaluated in ocean observation sensitivity experiments with current and future generation CDA and S2S prediction systems. Investments in the new ocean observations as well as model and DA system developments can lead to substantial returns on cost savings from disaster mitigation as well as socio–economic decisions that use S2S forecast information.
  • 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.