Penny Stephen G.

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Penny
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Stephen G.
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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
    Best practice strategies for process studies designed to improve climate modeling
    (American Meteorological Society, 2020-10-01) Sprintall, Janet ; Coles, Victoria J. ; Reed, Kevin A. ; Butler, Amy H. ; Foltz, Gregory R. ; Penny, Stephen G. ; Seo, Hyodae
    Process studies are designed to improve our understanding of poorly described physical processes that are central to the behavior of the climate system. They typically include coordinated efforts of intensive field campaigns in the atmosphere and/or ocean to collect a carefully planned set of in situ observations. Ideally the observational portion of a process study is paired with numerical modeling efforts that lead to better representation of a poorly simulated or previously neglected physical process in operational and research models. This article provides a framework of best practices to help guide scientists in carrying out more productive, collaborative, and successful process studies. Topics include the planning and implementation of a process study and the associated web of logistical challenges; the development of focused science goals and testable hypotheses; and the importance of assembling an integrated and compatible team with a diversity of social identity, gender, career stage, and scientific background. Guidelines are also provided for scientific data management, dissemination, and stewardship. Above all, developing trust and continual communication within the science team during the field campaign and analysis phase are key for process studies. We consider a successful process study as one that ultimately will improve our quantitative understanding of the mechanisms responsible for climate variability and enhance our ability to represent them in climate models.