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ArticleSeptember 2017's geoeffective space weather and impacts to Caribbean radio communications during Hurricane response(John Wiley & Sons, 2018-09-03) Redmon, Robert J. ; Seaton, Daniel B. ; Steenburgh, Robert ; He, Jing ; Rodriguez, Juan V.Between 4 and 10 September 2017, multiple solar eruptions occurred from active region AR12673. NOAA's and NASA's well‐instrumented spacecraft observed the evolution of these geoeffective events from their solar origins, through the interplanetary medium, to their geospace impacts. The 6 September X9.3 flare was the largest to date for the nearly concluded solar cycle 24 and, in fact, the brightest recorded since an X17 flare in September 2005, which occurred during the declining phase of solar cycle 23. Rapid ionization of the sunlit upper atmosphere occurred, disrupting high‐frequency communications in the Caribbean region while emergency managers were scrambling to provide critical recovery services caused by the region's devastating hurricanes. The 10 September west limb eruption resulted in the first solar energetic particle event since 2012 with sufficient flux and energy to yield a ground level enhancement. Spacecraft at L1, including DSCOVR, sampled the associated interplanetary coronal mass ejections minutes before their collision with Earth's magnetosphere. Strong compression and erosion of the dayside magnetosphere occurred, placing geosynchronous satellites in the magnetosheath. Subsequent geomagnetic storms produced magnificent auroral displays and elevated hazards to power systems. Through the lens of NOAA's space weather R‐S‐G storm scales, this event period increased hazards for systems susceptible to elevated “radio blackout” (R3‐strong), “solar radiation storm” (S3‐strong), and “geomagnetic storm” (G4‐severe) conditions. The purpose of this paper is to provide an overview of the September 2017 space weather event, and a summary of its consequences, including forecaster, post‐event analyst, and communication operator perspectives.
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ArticleHow the source depth of coastal upwelling relates to stratification and wind(American Geophysical Union, 2021-11-29) He, Jing ; Mahadevan, AmalaWind-driven coastal upwelling is an important process that transports nutrients from the deep ocean to the surface, fueling biological productivity. To better understand what affects the upward transport of nutrients (and many other properties such as temperature, salinity, oxygen, and carbon), it is necessary to know the depth of source waters (i.e., “source depth”) or the density of source waters (“source density”). Here, we focus on the upwelling driven by offshore Ekman transport and present a scaling relation for the source depth and density by considering a balance between the wind-driven upwelling and eddy-driven restratification processes. The scaling suggests that the source depth varies as (τ/N)1/2, while the source density goes as (τ1/2N3/2), where τ is the wind stress and N is the stratification. We test these relations using numerical simulations of an idealized coastal upwelling front with varying constant wind forcing and initial stratification, and we find good agreement between the theory and numerical experiments. This work highlights the importance of considering stratification in wind-driven upwelling dynamics, especially when thinking about how nutrient transport and primary production of coastal upwelling regions might change with increased ocean warming and stratification.
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ArticleSinking flux of particulate organic matter in the oceans: Sensitivity to particle characteristics(Nature Research, 2020-03-27) Omand, Melissa M. ; Govindarajan, Rama ; He, Jing ; Mahadevan, AmalaThe sinking of organic particles produced in the upper sunlit layers of the ocean forms an important limb of the oceanic biological pump, which impacts the sequestration of carbon and resupply of nutrients in the mesopelagic ocean. Particles raining out from the upper ocean undergo remineralization by bacteria colonized on their surface and interior, leading to an attenuation in the sinking flux of organic matter with depth. Here, we formulate a mechanistic model for the depth-dependent, sinking, particulate mass flux constituted by a range of sinking, remineralizing particles. Like previous studies, we find that the model does not achieve the characteristic ‘Martin curve’ flux profile with a single type of particle, but instead requires a distribution of particle sizes and/or properties. We consider various functional forms of remineralization appropriate for solid/compact particles, and aggregates with an anoxic or oxic interior. We explore the sensitivity of the shape of the flux vs. depth profile to the choice of remineralization function, relative particle density, particle size distribution, and water column density stratification, and find that neither a power-law nor exponential function provides a definitively superior fit to the modeled profiles. The profiles are also sensitive to the time history of the particle source. Varying surface particle size distribution (via the slope of the particle number spectrum) over 3 days to represent a transient phytoplankton bloom results in transient subsurface maxima or pulses in the sinking mass flux. This work contributes to a growing body of mechanistic export flux models that offer scope to incorporate underlying dynamical and biological processes into global carbon cycle models.
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ThesisModeling ocean transport and its biogeochemical impacts at global, regional, and sub-meso scales(Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2023-06) He, Jing ; Mahadevan, AmalaImproving understanding of how carbon is cycled through the ocean is crucial for predicting, mitigating, and adapting to climate change. This thesis explores how horizontal and vertical currents at different scales impact biogeochemical cycling through the redistribution of tracers such as alkalinity, nutrients, and carbon. Starting at the large scale in Chapter 2, we use a mesoscale-permitting global ocean model to investigate ocean alkalinity enhancement as a negative emissions technology. We find that local ocean dynamics are crucial for determining optimal alkalinity addition locations that maximize carbon removal, while minimizing adverse ecological impacts. Among the best locations identified are coastal upwelling systems, which are also regions of high primary productivity due to the large influx of nutrients to the surface. We take a closer look at coastal upwelling systems in Chapter 3 to identify the dynamics that impact source waters of steady-state upwelling at a regional scale, and we propose a scaling relation in which wind stress and stratification sets the upwelling source depth. Looking more closely at an upwelling front in a high-resolution submesoscale-permitting model, we see enhanced vertical velocities that reach 𝒪(100 m d−1). These submesoscale vertical velocities can enhance vertical transport, but they are very difficult to measure. In Chapter 4, we demonstrate the possibility of diagnosing the 3D submesoscale vertical velocity field from remotely-observable surface ocean observations with machine learning, which motivates future satellite missions for high-resolution remote-sensing of the surface ocean. Finally in Chapter 5, we evaluate the importance of resolving smaller scale submesoscale dynamics on the vertical transport of nutrient and phytoplankton carbon biomass in upwelling systems.
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ArticleVertical velocity diagnosed from surface data with machine learning(American Geophysical Union, 2024-03-11) He, Jing ; Mahadevan, AmalaSubmesoscale vertical velocities, w, are important for the oceanic transport of heat and biogeochemical properties, but observing w is challenging. New remote sensing technologies of horizontal surface velocity at O(1) km resolution can resolve surface submesoscale dynamics and offer promise for diagnosing w subsurface. Using machine learning models, we examine relationships between the three-dimensional w field and remotely observable surface variables such as horizontal velocity, density, and their horizontal gradients. We evaluate the machine learning models' sensitivities to different inputs, spatial resolution of surface fields, the addition of noise, and information about the subsurface density. We find that surface data is sufficient for reconstructing w, and having high resolution horizontal velocities with minimal errors is crucial for accurate w predictions. This highlights the importance of finer scale surface velocity measurements and suggests that data-driven methods may be effective tools for linking surface observations with vertical velocity and transport subsurface.