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14 results for “vertical mixing”
nextGEMS: Output of the WP6 ocean vertical mixing sensitivity runs (timeseries)
<p>In work package 6 of the nextGEMS project, several ocean-only model runs were performed with FESOM (Version 2.0) and ICON-O (Version 2.6.6), to test the sensitivity of the upper tropical Atlantic to different settings of the vertical mixing scheme. Two different mixing schemes were tested: TKE and KPP. For TKE, we tested different settings of the c_k parameter (0.1, 0.2 and 0.3), and for KPP different settings of the critical bulk Richardson number (0.3 and 0.27). These runs were done with both ICON-O and FESOM, to enable a comparison of the effects of the vertical mixing settings across different models. From ICON-O only, there are some additional TKE runs available, where we increased the interior ocean background mixing, and switched on the Langmuir turbulence parameterisation. There is also an ICON-O run which uses the FESOM default forcing bulk formulae, to check how much of the differences between the models originates from their different default bulk formulae.<br> <br> All model runs are ocean only, forced with hourly ERA5 reanalysis data. The horizontal resolution is 10km (for FESOM, the extratropical regions have a coarser grid).</p> <p>Here we provide high-frequency (3 hourly) time series of the model output, at selected locations in the tropical Atlantic where observational data are available for comparison in the simulated time range (2014 and 2015). We provide the following locations here:</p> <ul> <li>0N, 10W</li> <li>0N, 23W</li> <li>11.5N, 23W</li> <li>15N, 38W</li> <li>11N, 21.2W</li> <li>17.6N, 24.3W</li> </ul>
Probing the Extent of Vertical Mixing in Brown Dwarf Atmospheres with Disequilibrium Chemistry
<p><strong>OVERVIEW</strong></p> <p>The substellar atmospheric models described in <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022)</a> are presented here. The grid of these 1D radiative-convective atmospheric models was computed using the newly released open-source climate code PICASO 3.0 (<a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>). The grid consists of four parameters – the effective temperature (T<sub>eff</sub>), gravity (log(g)), K<sub>zz</sub> in the radiative zones, and mixing length in the convective zones. Models with T<sub>eff </sub> between 400-1000 K with an increment of 25 K are included. log(g) has been varied from 4.5 to 5.5 with an increment of 0.25 dex. The K<sub>zz</sub> in the radiative zone has been varied between 1x, 0.01x, and 100x the parametrization presented in <a href="https://ui.adsabs.harvard.edu/abs/2022ExA....53..279M/abstract">Moses et al. (2021)</a>, whereas the convective mixing length has been between the atmospheric pressure scale height and 0.1x the scale height.</p> <p>There are three types of files released here – atmospheric composition files (TP_chemistry), thermal emission spectra files (spectra), and atmospheric Kzz profile files (TP_kz). </p> <p><strong>ATMOSPHERIC COMPOSITION</strong></p> <p>The atmospheric composition files are located in the folder TP_chemistry. These files have the temperature structure of the atmosphere as a function of pressure accompanied by the volume mixing ratio of 37 gases as a function of pressure. </p> <p>The TP_chemistry files are named following the format “profile_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat", where [factor1] denotes the multiplier used for the radiative zone Kzz and can vary between ‘x0pt01’, ‘x1’, and ‘x100’. [factor2] denotes the multiplier for the mixing length and can vary between ‘1’ and ‘0pt1’. [Teff] and [gravity] denote the Teff and gravity of the models used. A simple code snippet to read and plot these files is presented below.</p> <p><strong>SPECTRA</strong></p> <p>The spectra files are located in the folder "spectra_highres_1", "spectra_highres_2", "spectra_highres_3", and "spectra_highres_4". These files have the thermal emission spectra between 0.3-30 microns calculated using the computed models. The native spectral resolution of these calculations is at an R = 500,000, but <strong>please be aware that these spectra should always be binned down to a resolution of R = 50,000 or less before usage</strong>. This means that these spectra should only be used to interpret datasets with a spectral resolution of 50,000 or less. Please contact the authors if higher resolution spectra are needed. The spectra have been uploaded in three different folders to make the file sizes manageable for transfer.</p> <p>The spectra files are also similarly named using the format “spectra_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.tar.gz". These files can be directly read into a Python pandas dataframe using </p> <pre><code class="language-python">pd.read_csv(filename, compression='gzip')</code></pre> <p> The first column of the file is wavenumbers in cm<sup>-1, </sup>which can be converted to wavelength in microns by wavelength [microns] =10000/wavenumbers[cm<sup>-1</sup>]. The second column of the file is flux in erg/s/cm<sup>2</sup>/cm. Note that these fluxes need to be multiplied with R<sup>2</sup>/D<sup>2</sup> before comparing them with the typically observed flux of brown dwarfs/exoplanets. R is the radius of the object, and D is the distance here. A tutorial to convert these fluxes to other units is present in <a href="https://natashabatalha.github.io/picaso/notebooks/6_BrownDwarfs.html#Convert-to-F_\nu-Units-and-Regrid">this link</a>. A binned-down version (R=15,000) of these high-resolution spectra can also be found in the "spectra_lowres" folder. These can be used for datasets that have a maximum spectral resolution of 15,000.</p> <p><strong>K<sub>zz</sub> PROFILE</strong></p> <p>The K<sub>zz </sub>as a function of pressure for each model is presented in these files. The K<sub>zz</sub> is reported in cm<sup>2</sup>/s. These files are also similarly named using the format “kz_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat". The columns of the files are pressure in bars, the temperature in K, and Kzz in cm<sup>2</sup>/s.</p> <p> </p> <p><strong>EXAMPLE PYTHON CODE TO READ AND PLOT COMPOSITION FILES</strong></p> <pre><code class="language-python">import numpy as np import pandas as pd import matplotlib.pyplot as plt grav = np.array([316,562,1000,1780,3160]) Teff=np.array([400,425,450,475,500,525,550,575,600,625,650,675,700,725,750,775,800,825,850,875,900,925,950,975,1000]) factor1 = np.array(['x0pt01','x1','x100']) factor2 = np.array(['1','0pt1']) file ="profile_sc_qt_rz_"+factor1[0]+"_cz_"+factor2[0]+"_"+str(Teff[14])+"_grav_"+str(grav[14])+"_mh_+0.0_sm_NA.dat" df = pd.read_csv(file, sep="\t") # Plot T(P) profile plt.ylim(100,1e-4) plt.semilogy(df['temperature'],df['pressure']) plt.show() # Plot H2O mixing ratio profile plt.ylim(100,1e-4) plt.loglog(df['H2O'],df['pressure']) plt.show()</code></pre> <p><strong>CREDITS</strong></p> <p>If you use these tables, please cite <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022, Astrophysical Journal, in press.)</a></p> <p> </p>
data for the paper "Role of Vertical Mixing in the Upper Ocean in the Seasonal Variation of Arctic Amplification" in 2nd round peer review
<p>Here are all data for the paper "Role of Vertical Mixing in the Upper Ocean in the Seasonal Variation of Arctic Amplification", which is still during its peer review period. </p>
Roles of regional transport and vertical mixing in aerosol pollution in Shanghai over the COVID-19 lockdown period observed above urban canopy
<p>Abstract</p> <p>Regional transport and vertical mixing are important for aerosol pollution of megacities, but their roles are often challenging to assess via ground observations. In this study, we measured aerosol chemical composition simultaneously above urban canopy (Shanghai Tower, 609 m), a site representative of aerosols from regional scale, and a nearby ground site and investigated the roles of regional transport and vertical mixing over 2020 COVID-19 lockdown period (Jan.1-Apr. 18), when local emissions were first drastically reduced and then recovered. During the lockdown period, regional transport was the major source of most aerosol species (organics, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup>, and NH<sub>4</sub><sup>+</sup>) at both heights according to the high correlation coefficients (R=0.81~0.87) between both heights. Correlation coefficients and vertical ratios (609 m/ground) of most aerosol components showed similar diurnal variations with the evolution of planetary boundary layer height, indicating the role of vertical mixing in aerosol pollution. Moreover, the concentrations of aerosol components at 609 m generally preceded those at ground level by 1~2 h, indicating that aerosols were first transported at upper boundary layer, and then were mixed downwards to ground level. At 609 m, highly oxidized oxygenated organic aerosol (OOA; a surrogate of secondary organic aerosol (SOA)) dominated in organic aerosol (≥75%). The high correlations (R=0.96) between OOA and hydrocarbon-like organic aerosol (HOA; a surrogate of primary organic aerosol (POA)) at 609 m indicated that they originated similarly from regional transport. This study highlights the importance of regional joint prevention and control of pollutant emissions and observation above urban canopy.</p> <p> </p> <p>The data files are provided for readers to evaluate this research.</p>
Data from: Shear-induced vertical mixing in a stratified Saharan Air Layer
Open the record for dataset details and reuse information.
Estimates of surface layer net community production based on underway Lagrangian measurements of the dissolved O2/Ar ratio using Equilibrator Inlet Mass Spectrometry (EIMS), based on both steady-state and non-steady-state assumptions of the mixed-layer biological oxygen budget. Also included are estimates of the potential contribution of vertical fluxes: advection, eddy diffusion, and entrainment.
The ratio of dissolved oxygen to argon in surface seawater is frequently employed to estimate rates of net community production (NCP) in the oceanic mixed layer. The in situ O2/Ar-based method accounts for many physical factors that influence oxygen concentrations in the surface ocean, permitting isolation of the biological oxygen signal produced by the balance of photosynthesis and respiration. However, this technique traditionally relies upon several assumptions when calculating the mixed layer O2/Ar budget, most notably the absence of vertical fluxes of O2/Ar and the existence of a steady-state balance between net productivity and the air-sea gas exchange of biological oxygen. Employing a Lagrangian study design and leveraging data outputs from a regional physical oceanographic model, we conducted in situ measurements of O2/Ar in the California Current Ecosystem in spring 2016 and summer 2017 to evaluate these assumptions within a ‘worst-case’ field environment. Quantifying the magnitude of vertical fluxes and comparing NCP estimates obtained using steady-state versus non-steady-state assumptions, we find the importance of the non-steady-state term to be considerable, also observing significant potential effects from vertical flux terms, particularly advection. Additionally, we observe strong diel variability in O2/Ar and calculated NCP rates at multiple stations. Our results reemphasize the importance of accounting for vertical fluxes when interpreting O2/Ar-derived NCP data as well as the potentially large effect of non-steady-state conditions, including diel cycles in surface O2/Ar that can bias interpretation of NCP data based on local productivity and the time of day at which measurements were made.
ERA-I version data for the paper "Role of Vertical Mixing in the Upper Ocean in the Seasonal Variation of Arctic Amplification"
<p>All the data, used to draw figures in the manuscript are uploaded. The variables' description have been well defined in the manuscript. </p>
MOM6 Model Output from "Improved Upper Ocean Vertical Mixing in the Equatorial Oceans in NOAA/GFDL's OM4 Model"
<p>Datasets produced during the MOM6 experiments described by "Improved Upper Ocean Vertical Mixing in the Equatorial Oceans in NOAA/GFDL's OM4 Model".</p> <p>These datasets are needed to reproduce the figures in the version of this manuscript as submitted to ESS.</p> <p>The location of the notebooks for creating figures from these datasets is indicated in the manuscript.</p>
Code and data for: The evolution of light and vertical mixing across a phytoplankton ice-edge bloom
<p>This is a first release of the code (and the data and figures it produced) used by Randelhoff et al. for "The evolution of light and vertical mixing across a phytoplankton ice-edge bloom", Elementa: Science of the Anthropocene (2019).</p>
The ouput simulation data of the Exo-FMS GCM and of the post-processing with gCMCRT for Effects of the internal temperature on vertical mixing and on cloud structures in Ultra Hot Jupiters
<p>The datasets contains the ouput simulation data of the Exo-FMS GCM and of the post-processing with gCMCRT for Effects of the internal temperature on vertical mixing and on cloud structures in Ultra Hot Jupiters</p> <p>Context. The vertical mixing in hot Jupiter atmospheres plays a critical role in the formation and spacial distribution of cloud particles<br>in their atmospheres. This affects the observed spectra of a planet through cloud opacity, which can be influenced by the degree of<br>cold trapping of refractory species in the deep atmosphere.<br>Aims. We aim to isolate the effects of the internal temperature on the mixing efficiency in the atmospheres of Ultra Hot Jupiters (UHJ)<br>and the spacial distribution of cloud particles across the globe.<br>Methods. We couple a simplified tracer based cloud model, picket fence radiative-transfer scheme and mixing length theory to the<br>Exo-FMS general circulation model. We run the model for five different internal temperatures at typical UHJ atmosphere system<br>parameters.<br>Results. Our results show the convective eddy diffusion coefficient remains low throughout the vast majority of the atmosphere, with<br>mixing dominated by advective flows. However, some regions can show convective mixing in the upper atmosphere for colder interior<br>temperatures. The vertical extent of the clouds is reduced as the internal temperature is increased. Additionally, a global cloud layer<br>gets formed below the radiative-convective boundary (RCB) in the cooler cases.<br>Conclusions. Convection is generally strongly inhibited in UHJ atmospheres above the RCB due to their strong irradiation. Convective<br>mixing plays a minor role in keeping cloud particles aloft in ultra hot Jupiters with warm interiors. Our results suggest isolated upper<br>atmosphere regions above cold interiors may exhibit strong convective mixing, allowing aerosols to be better retained in these areas.</p>
Data of Numerical simulation data and experimental data for vertical mixing schemes
<p>This dataset contains the Numerical simulation data and experimental data in paper_data file. The new scheme is saved in the 'mypackage' folder in paper_code file.</p>
Dataset and code for the manuscript 'Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks'
<p>This repository contains the code and data used in the manuscript 'Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks'. <br> Manuscript authors: Dr. Aakash Sane, Dr. Brandon G. Reichl, Dr. Alistair Adcroft, Dr. Laure Zanna<br> Manuscript preprint link: https://doi.org/10.48550/arXiv.2306.09045</p>
Data from: Predator prey games in multiple habitats reveal mixed strategies in diel vertical migration
Open the record for dataset details and reuse information.
Induction of inverted morphology in brain organoids by vertical-mixing bioreactors
GEO Series GSE184409. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
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