An official website of the United States government
Abstract Rainfall alters the physical and chemical properties of the surface ocean, and its effect on ocean skin temperature and surface heat fluxes is poorly represented in many air‐sea interaction models. We present radiometric observations of ocean skin temperature, near‐surface (5 cm) temperature from a towed thermistor, and bulk atmospheric and oceanic variables, for 69 rain events observed over the course of 4 months in the Indian Ocean as part of the DYNAMO project. We test a state‐of‐the‐art prognostic model developed by Bellenger et al. (2017,https://doi.org/10.1002/2016JC012429) to predict ocean skin temperature in the presence of rain, and demonstrate a physically motivated modification to the model that improves its performance with increasing rain rate. We characterize the vertical skin‐bulk temperature gradient induced by rain and find that it levels off at high rain rates, suggestive of a transition in skin‐layer physics that has been previously hypothesized in the literature. We also quantify the small bias that will be present in turbulent sensible heat fluxes parameterized from ocean temperature measurements made at typical “bulk” depths during a rain event. Finally, a wind threshold is observed above which the surface ocean remains well‐mixed during a rain event; however, the skin temperature is observed to decrease at all wind speeds in the presence of rain.
more »
« less
The Effect of Resolution on Vertical Heat and Carbon Transports in a Regional Ocean Circulation Model of the Argentine Basin
Abstract Simulations of the Argentine Basin have large uncertainties associated with quantities such as air‐sea exchanges of heat and carbon in current generation climate models and ocean reanalysis products. This is due to the complex topography, profound undersampling until recent years, and strong currents and mixing of subpolar and subtropical water masses in the basin. Because mixing of water masses is important here, model resolution is hypothesized to play an important role in estimating ocean quantities and determining overall budgets. We construct three regional ocean models with biogeochemistry at 1/3°, 1/6°, and 1/12° resolutions for the year 2017 to investigate heat and carbon dynamics in the region and determine the effect of model resolution on these dynamics. Initial conditions and boundary forcing from BSOSE (the Biogeochemical Southern Ocean State Estimate (Verdy & Mazloff, 2017),https://doi.org/10.1002/2016JC012650) and atmospheric forcing from ERA5 are used. The models are evaluated for accuracy by comparing output to Argo and BGC‐Argo float profiles, BSOSE, and other reanalyses and mapped products. We then quantify the effect of resolution on model upper ocean heat and carbon transport and the associated air‐sea exchanges. We determine that increasing the resolution from 1/3° to 1/12° enhances the upward vertical transport and surface exchanges of heat but causes no significant effect on surface carbon fluxes despite enhancing downward transport of anomalous DIC.
more »
« less
- Award ID(s):
- 1936222
- PAR ID:
- 10360156
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Oceans
- Volume:
- 126
- Issue:
- 7
- ISSN:
- 2169-9275
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract In tidewater glacier fjords, subglacial discharge drives a significant mixing mechanism near glacier fronts and drives a strong exchange flow. Numerous studies (Cowton et al., 2015,https://doi.org/10.1002/2014jc010324; Slater et al., 2017,https://doi.org/10.1002/2016gl072374) have utilized a parameterization for buoyant plume theory to force fjord scales systems, but neglect to parameterize the outflowing of the plume away from the glacial wall after it has reached its neutral density. In this study, a new model framework, ROMS‐ICEPLUME, is developed to parameterize the rising and initial outflowing stage of subglacial discharge plumes in the Regional Ocean Modeling System. The coupled model applies a novel parameterization algorithm to prescribe the velocity and vertical extent of the outflowing plume, which reduces numerical instability and improves model performance. The model framework is tested with a quasi‐realistic forcing using observations of a subglacial discharge plume hydrographic surveys collected from a Greenland fjord. We find that the new model framework is able to reproduce the strong outflowing plume and the compensating inflow at depth, with a spatial structure that correlates well with in‐situ observations. On the other hand, the model framework without the new parameterization algorithm fails to capture the outflowing plume structure. Thus, our new framework for parameterizing subglacial discharge plumes is an improvement from previous coupled model frameworks, and is a promising tool toward advancing our understanding of circulation in tidewater glacier fjords.more » « less
-
Abstract Obtaining meteoroid mass from head echo radar cross section depends on the assumed plasma density distribution around the meteoroid. An analytical model presented in Dimant and Oppenheim (2017a,https://doi.org/10.1002/2017JA023960; 2017b,https://doi.org/10.1002/2017JA023963) and simulation results presented in Sugar et al. (2018,https://doi.org/10.1002/2018JA025265) suggest the plasma density distribution is significantly different than the spherically symmetric Gaussian distribution used to calculate meteoroid masses in many previous studies. However, these analytical and simulation results ignored the effects of electric and magnetic fields and assumed quasi‐neutrality. This paper presents results from the first particle‐in‐cell simulations of head echo plasma that include electric and magnetic fields. The simulations show that the fields change the ion density distribution by less than ∼2% in the meteor head echo region, but the electron density distribution changes by up to tens of percent depending on the location, electron energies, and magnetic field orientation with respect to the meteoroid path.more » « less
-
Abstract Low‐frequency earthquakes are a seismic manifestation of slow fault slip. Their emergent onsets, low amplitudes, and unique frequency characteristics make these events difficult to detect in continuous seismic data. Here, we train a convolutional neural network to detect low‐frequency earthquakes near Parkfield, CA using the catalog of Shelly (2017),https://doi.org/10.1002/2017jb014047as training data. We explore how varying model size and targets influence the performance of the resulting network. Our preferred network has a peak accuracy of 85% and can reliably pick low‐frequency earthquake (LFE) S‐wave arrival times on single station records. We demonstrate the abilities of the network using data from permanent and temporary stations near Parkfield, and show that it detects new LFEs that are not part of the Shelly (2017),https://doi.org/10.1002/2017jb014047catalog. Overall, machine‐learning approaches show great promise for identifying additional low‐frequency earthquake sources. The technique is fast, generalizable, and does not require sources to repeat.more » « less
-
Abstract We compared the performance of DREAM3D simulations in reproducing the long‐term radiation belt dynamics observed by Van Allen Probes over the entire year of 2017 with various boundary conditions (BCs) and model inputs. Specifically, we investigated the effects of three different outer boundary conditions, two different low‐energy boundary conditions for seed electrons, four different radial diffusion (RD) coefficients (DLL), four hiss wave models, and two chorus wave models from the literature. Using the outer boundary condition driven by GOES data, our benchmark simulation generally well reproduces the observed radiation belt dynamics insideL* = 6, with a better model performance at lowerμthan higherμ, whereμis the first adiabatic invariant. By varying the boundary conditions and inputs, we find that: (a) The data‐driven outer boundary condition is critical to the model performance, while adding in the data‐driven seed population doesn't further improve the performance. (b) The model shows comparable performance withDLLfrom Brautigam and Albert (2000,https://doi.org/10.1029/1999ja900344), Ozeke et al. (2014,https://doi.org/10.1002/2013ja019204), and Liu et al. (2016,https://doi.org/10.1002/2015gl067398), while withDLLfrom Ali et al. (2016,https://doi.org/10.1002/2016ja023002) the model shows less RD compared to data. (c) The model performance is similar with data‐based hiss models, but the results show faster loss is still needed inside the plasmasphere. (d) The model performs similarly with the two different chorus models, but better capturing the electron enhancement at higherμusing the Wang et al. (2019,https://doi.org/10.1029/2018ja026183) model due to its stronger wave power, since local heating for higher energy electrons is under‐reproduced in the current model.more » « less
