Wave breaking induced bubbles contribute a significant part of air‐sea gas fluxes. Recent modeling of the sea state dependent CO2flux found that bubbles contribute up to ∼40% of the total CO2air‐sea fluxes (Reichl & Deike, 2020,
Shelfbreak exchange processes have been studied extensively in the Middle Atlantic Bight. An important process occurring during stratified conditions is the Salinity Maximum Intrusion. These features are commonly observed at the depth of the seasonal pycnocline, and less frequently at the surface and bottom. Data collected from NOAA's National Marine Fisheries Service Ecosystem Monitoring program as well as data collected from the fishing industry in Rhode Island show that the middepth intrusions are now occurring much more frequently than was reported in a previous climatology of the intrusions (Lentz, 2003,
- NSF-PAR ID:
- 10369431
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Oceans
- Volume:
- 127
- Issue:
- 7
- ISSN:
- 2169-9275
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract https://doi.org/10.1029/2020gl087267 ). In this study, we implement the sea state dependent bubble gas transfer formulation of Deike and Melville (2018,https://doi.org/10.1029/2018gl078758 ) into a spectral wave model (WAVEWATCH III) incorporating the spectral modeling of the wave breaking distribution from Romero (2019,https://doi.org/10.1029/2019gl083408 ). We evaluate the accuracy of the sea state dependent gas transfer parameterization against available measurements of CO2gas transfer velocity from 9 data sets (11 research cruises, see Yang et al. (2022,https://doi.org/10.3389/fmars.2022.826421 )). The sea state dependent parameterization for CO2gas transfer velocity is consistent with observations, while the traditional wind‐only parameterization used in most global models slightly underestimates the observations of gas transfer velocity. We produce a climatology of the sea state dependent gas transfer velocity using reanalysis wind and wave data spanning 1980–2017. The climatology shows that the enhanced gas transfer velocity occurs frequently in regions with developed sea states (with strong wave breaking and high significant wave height). The present study provides a general sea state dependent parameterization for gas transfer, which can be implemented in global coupled models. -
more » « less
Abstract We present empirical conductance relations that are derived from incoherent scatter radar observations and correlated with all sky imager observations to identify the morphology of the aurora. We use 75,461 events collected using the Poker Flat Incoherent Scatter Radar (PFISR) with associated all sky imagers observations spanning the years 2012–2016. In addition to classifying these events based on auroral morphology, we estimated the Hall and Pedersen conductance and the differential number flux from which the energy flux and the average energy can be calculated. The differential number flux was estimated using the maximum entropy inversion method described in Semeter and Kamalabadi (2005,
https://doi.org/10.1029/2004RS003042 ), but now incorporating the Fang et al. (2010,https://doi.org/10.1029/2010GL045406 ) ionization model. The main results of this investigation are the power law equations that describe the median, 90th, and 10th percentile Hall and Pedersen conductance as a function of energy flux and average energy. These power law fits are performed for different auroral morphology including all events, discrete, diffuse, and pulsating auroral events. The median Pedersen conductance is found to be in good agreement with past empirical conductance specifications by Robinson et al. (1987,https://doi.org/10.1029/JA092iA03p02565 ); however, the median Hall conductance from the PFISR observations is found to be larger than the empirical Hall conductance formulas by Robinson et al. (1987,https://doi.org/10.1029/JA092iA03p02565 ). Pulsating aurora is found to be the most frequently occurring auroral morphology. Furthermore, pulsating aurora has an important contribution to Hall conductance since it has higher average energies than discrete aurora. The results from this investigation are applicable to space weather models and may enable better agreement between model‐data comparisons. -
more » « less
Abstract We report on the mountain top observation of three terrestrial gamma‐ray flashes (TGFs) that occurred during the summer storm season of 2021. To our knowledge, these are the first TGFs observed in a mountaintop environment and the first published European TGFs observed from the ground. A gamma‐ray sensitive detector was located at the base of the Säntis Tower in Switzerland and observed three unique TGF events with coincident radio sferic data characteristic of TGFs seen from space. We will show an example of a “slow pulse” radio signature (Cummer et al., 2011,
https://doi.org/10.1029/2011GL048099 ; Lu et al., 2011,https://doi.org/10.1029/2010JA016141 ; Pu et al., 2019,https://doi.org/10.1029/2019GL082743 ; Pu et al., 2020,https://doi.org/10.1029/2020GL089427 ), a −EIP (Lyu et al., 2016,https://doi.org/10.1002/2016GL070154 ; Lyu et al., 2021,https://doi.org/10.1029/2021GL093627 ; Wada et al., 2020,https://doi.org/10.1029/2019JD031730 ), and a double peak TGF associated with an extraordinarily powerful and complicated positive‐polarity sferic, where each TGF peak is possibly preceded by a short burst of stepped leader emission. -
more » « less
Abstract We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML‐AIM). ML‐AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020,
https://doi.org/10.1029/2020JA027908 ), the FAC‐derived auroral conductance model of Robinson et al. (2020,https://doi.org/10.1029/2020JA028008 ), and the solar irradiance conductance model of Moen and Brekke (1993,https://doi.org/10.1029/92gl02109 ). The ML‐AIM inputs are 60‐min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML‐AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML‐AIM produces physically accurate ionospheric potential patterns such as the two‐cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC ) from ML‐AIM, the Weimer (2005,https://doi.org/10.1029/2004ja010884 ) model, and the Super Dual Auroral Radar Network (SuperDARN) data‐assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML‐AIM than others. ML‐AIM is unique and innovative because it predicts ionospheric responses to the time‐varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005,https://doi.org/10.1029/2004ja010884 ) designed to provide a quasi‐static ionospheric condition under quasi‐steady solar wind/IMF conditions. Plans are underway to improve ML‐AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times. -
more » « less
Abstract Recent advances in remote sensing of solar‐induced chlorophyll fluorescence (SIF) have garnered wide interest from the biogeoscience and Earth system science communities, due to the observed linearity between SIF and gross primary productivity (GPP) at increasing spatiotemporal scales. Three recent studies, Maguire et al., (2020,
https://doi.org/10.1029/2020GL087858 ), He et al. (2020,https://doi.org/10.1029/2020GL087474 ), and Marrs et al. (2020,https://doi.org/10.1029/2020GL087956 ) highlight a nonlinear relationship between fluorescence and photochemical yields and show empirical evidence for the decoupling of SIF, stomata, and the carbon reactions of photosynthesis. Such mechanistic studies help advance our understanding of what SIF is and what it is not. We argue that these findings are not necessarily contradictory to the linear SIF‐GPP relationship observed at the satellite scale and provide context for where, when, and why fluorescence and photosynthesis diverge at smaller spatiotemporal scales. Understanding scale dependencies of remote sensing data is crucial for interpreting SIF as a proxy for GPP.
