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47 results for “mixed layer”
On the cell broadening of mesoscale cellular convection in a well-mixed boundary layer
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Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations: Preprocessed Satellite and In-situ observation datasets
<p>This record includes all of the prepared data used in the manuscript, "Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations" (citation information forthcoming). As a part of this manuscript, we analyzed the ability for machine learning models to extract sea surface information (from salinity, temperature, sea height anomaly) to predict mixed layer depth. In this manuscript there are two experimental datasets: (1) info derived from CESM POP2 ocean model dataset (1989-1998), and (2) info derived from a combination of satellite sources and MLD from Argo profiles. More details below. </p> <p>All of these data files are preprocessed and organized to be used with the ml-ocean-bl github code found at https://github.com/NCAR/ml-ocean-bl/mloceanbl/.</p> <ul> <li><strong>CESM POP2 Ocean model dataset</strong></li> </ul> <p>Preprocessed sea surface salinity (SSS), temperature (SST), sea surface height anomalies (SSH), and ocean mixed layer depth (MLD, or HMXL) derived from the CESM POP2 Ocean model. Specifically, CESM POP2 model in a hindcast forced by JRA55do atmospheric reanalysis from 1958 to present and initialized with an oceanic climatology as in e.g. <a href="https://journals.ametsoc.org/view/journals/phoc/aop/JPO-D-20-0217.1/JPO-D-20-0217.1.xml">Deppenmeier et al. (2021)</a>. The model outputs include the ocean mixed layer depth (MLD), sea surface salinity (SSS), sea surface temperature (SST), and sea height anomaly (SSH) at a temporal frequency of 5-days and an approximate latitude and longitude resolution of 0.1 degrees.</p> <p>Relevant files:</p> <ol> <li>full_EPO.nc, full_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, MLD for the equatorial Pacific Ocean (EPO) and southern Indian Ocean (SIO) (see manuscript for details). Data is regridded onto a 1/2 degree lat/lon 5 day grid to correspond with data used for Argo datasets (see below).</li> </ul> </li> <li>clim_EPO.nc, clim_SIO.nc, clim_std_EPO.nc, std_clim_EPO.nc, std_clim_SIO.nc <ul> <li>NetCDF4 containing mean and standard deviation climatologies of SSS, SST, SSH, and MLD for EPO and SIO.</li> </ul> </li> <li>std_anomalies_EPO.nc, std_anomalies_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, and MLD standardized anomalies for EPO and SIO. This is the dataset directly used for training in aforementioned manuscript. Use with ml-ocean-bl/ml-ocean-test/data. </li> </ul> </li> </ol> <ul> <li><strong>Satellite and Argo datasets</strong></li> </ul> <p>Preprocessed satellite sea surface salinity (SSS), temperature (SST), and sea surface height anomalies (SSH) and Argo-based mixed layer depth (MLD) profiles. Original data can be found at:</p> <p>(SST): Remote Sensing Systems. 2017. MW optimum interpolated SST data set. Ver. 5.0. PO.DAAC, CA, USA. Further information available at at <a href="https://doi.org/10.5067/GHMWO-4FR05">https://doi.org/10.5067/GHMWO-4FR05</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/REMSS/mw_OI/v5.0/.</p> <p>(SSS): Oleg Melnichenko. 2018. Aquarius L4 Optimally Interpolated Sea Surface Salinity. Ver. 5.0. PO.DAAC, CA, USA. Further information at <a href="https://doi.org/10.5067/AQR50-4U7CS">https://doi.org/10.5067/AQR50-4U7CS</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SalinityDensity/aquarius/L4/IPRC/v5/7day. </p> <p>(SSH): Zlotnicki, Victor; Qu, Zheng; Willis, Joshua. 2019. SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1609. Ver. 1812. PO.DAAC, CA, USA. Information available at <a href="https://doi.org/10.5067/SLREF-CDRV2">https://doi.org/10.5067/SLREF-CDRV2</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SeaSurfaceTopography/merged_alt/L4/cdr_grid</p> <p>(MLD) Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019) processed dataset available at https://doi.org/10.5281/zenodo.4291175.</p> <p>Relevant files:</p> <ol> <li>https://github.com/NCAR/ml-ocean-bl/mloceanbl/preprocess_mld.py and .../preprocess_sss_sst_ssh.py. <ul> <li>Preprocessing code</li> </ul> </li> <li>sss_sst_ssh_anomalies.nc. <ul> <li>Regridded and resampled SSS, SST, SSH onto a 1/2 degree lat/lon 7day grid. Contains preprocessed seasonal data along with anomalies.</li> </ul> </li> <li> mldb_climatology_climatologystd_binned.nc <ul> <li>Smoothed argo-based mixed layer depths are used to calculate climatologies and standardized climatologies. 4 degree lat/lon gridded climatologies.</li> </ul> </li> <li>mldb_full_anomalies_stdanomalies_climatology_stdclimatology.nc <ul> <li>Contains the Argo profile-derived MLD, anomalies, standard anomalies, climatologies, and standardized climatologies with corresponding argo locations, times, and corresponding weeks. </li> </ul> </li> <li>equatorial_pacific_model_oi_re.nc, southern_indian_model_oi_re.nc <ul> <li>Model outputs for the Equatorial Pacific Ocean and Southern Indian Ocean. These gridded files contain the model outputs (vlcnn, vlcnn variance, OI, OI variance, reanalysis, and reanalysis variance - see manuscript for nomenclature details) at each of the 200 weeks available. It should be noted that, in the equatorial Pacific Ocean, the lat/lon location of (-138.75, -9.75) is masked during the training and filled with a NaN in the .nc files. </li> </ul> </li> </ol> <p> </p> <p> </p> <p>Contact D. Foster with any questions.</p> <p> </p>
A 1D Model for Nucleation of Ice from Aerosol Particles: An Application to a Mixed-Phase Arctic Stratus Cloud Layer
<p>A 1D Model for Nucleation of Ice from Aerosol Particles:</p> <p>An Application to a Mixed-Phase Arctic Stratus Cloud Layer</p> <p>Daniel A. Knopf<sup>1</sup>*, Israel Silber<sup>2</sup>, Nicole Riemer<sup>3</sup>, Ann M. Fridlind<sup>4</sup>, Andrew S. Ackerman<sup>4</sup></p> <p><sup>1</sup>School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA</p> <p><sup>2</sup>Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA</p> <p><sup>3</sup>Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, IL, USA</p> <p><sup>4</sup>NASA Goddard Institute for Space Studies, New York, NY, USA</p> <p> </p> <p>Corresponding author: Daniel Knopf (daniel.knopf@stonybrook.edu)</p> <p> </p> <p><strong>This repository contains all model output data in netCDF format to reproduce simulation results and corresponding figures given in above listed publication.</strong></p> <p>File name description:</p> <p>Type of parameterization: <em>INN, INAS, ABIFM</em></p> <p>INP treatment, diagnostic or prognostic: <em>diag, prog</em></p> <p>Aerosol size distribution:</p> <p><em>05mu</em>: monodisperse 0.5 μm diameter</p> <p><em>15mu</em>: monodisperse 0.5 μm diameter</p> <p><em>polydisperse</em>: polydisperse particle size distribution</p> <p>Model initialization and thermodynamic data: <em>model_data</em></p> <p>Aerosol/INP data: <em>aerosol_data</em>, <em>polydisperse_data</em></p> <p>Change in ice nucleation efficiency: <em>10X, 100X</em></p>
Regional controls on the sea ice-mixed layer depth relationship in the West Antarctic Peninsula (WAP)
<p>Data corresponding to "Regional controls of the sea ice-mixed layer depth relationship in the West Antarctic Peninsula (WAP)" (Bischof et al., in prep.).</p> <p>model_setup contains the input and code directories needed to run our model using MITgcm. data contains all model output used to plot the figures contained in the paper.</p> <p> </p>
Evaluation of multi-season convection permitting atmosphere - mixed layer ocean simulations of the Maritime Continent.
<p>Supporting data for figures in GMD draft paper: Evaluation of multi-season convection permitting atmosphere - mixed layer ocean simulations of the Maritime Continent.</p>
Data from: Shear-induced vertical mixing in a stratified Saharan Air Layer
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Dataset S2: Global Synechococcus pigment type distribution from metagenomes with co-located mixed layer depths and sea surface properties
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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.
Mixed Layer Depth Anomaly
<p>time is in days since 0</p>
Dataset linked to manuscript entitled "Changes in Arctic Stratification and Mixed Layer Depth Cycle, A Modeling Analysis" by Hordoir et al.
<p>This dataset allows the re-create the fingures showing changes in Arctic stratification and mixed layer depth, as in the manuscript. Additional information can be obtained by email.</p>
Glider and satellite high resolution monitoring of a mesoscale eddy in the algerian basin: Effects on the mixed layer depth and biochemistry
<p>Despite an extensive bibliography for the circulation of the Mediterranean Sea and its sub-basins, the debate on mesoscale dynamics and their impacts on bio-chemical processes is still open because of their intrinsic time scales and of the difficulties in their sampling. In order to clarify some of these processes, the “Algerian BAsin Circulation Unmanned Survey-ABACUS” project was proposed and realized through access to the JERICO Trans National Access (TNA) infrastructure between September and December 2014. In this framework, a deep glider cruise was carried out in the area between the Balearic Islands and the Algerian coast to establish a repeat line for monitoring of the basin circulation. During the mission a mesoscale eddy, identified on satellite altimetry maps, was sampled at high-spatial horizontal resolution (4 km) along its main axes and from the surface to 1000 m depth. Data were collected by a Slocum glider equipped with a pumped CTD and biochemical sensors that collected about 100 complete casts inside the eddy. In order to describe the structure of the eddy, in situ data were merged with next generation remotely sensed data: daily synoptic sea surface temperature (SST) and chlorophyll concentration (Chl-a) images from the MODIS satellites, as well as sea surface height and geostrophic velocities from AVISO. From its origin along the Algerian coast in the eastern part of the basin, the eddy propagated northwest at a mean speed of about 4 km/day, with a mean diameter of 112–130 km, mean amplitude of 15.7 cm; the eddy was clearly distinguished from the surrounding waters thanks to its higher SST and Chl-a values. Temperature and salinity values over the water column confirm the origin of the eddy from the Algerian Current (AC) showing the presence of recent Atlantic water in the surface layer and Levantine Intermediate Water (LIW) in the deeper layer. The eddy footprint is clearly evident in the multiparametric vertical sections conducted along its main axis.</p> <p>Deepening of temperature, salinity and density isolines at the center of the eddy is associated with variations in Chl-a, oxygen concentration and turbidity patterns. In particular, at 50 m depth along the eddy borders, Chl-a values are higher (1.1–5.2 μg/l) in comparison with the eddy center (0.5–0.7 μg/l) with maximum values found in the southeastern sector of the eddy.</p> <p>Calculation of geostrophic velocities along transects and vertical quasi-geostrophic velocities (QG-w) over a regular 5 km grid from the glider data helped to describe the mechanisms and functioning of the eddy. QG-w presents an asymmetric pattern, with relatively strong downwelling in the western part of the eddy and upwelling in the southeastern part. This asymmetry in the vertical velocity pattern, which brings LIW into the euphotic layer as well as advection from the northeastern sector of the eddy, may explain the observed increases in Chl-a values</p>
Figure 3 in Autumn community structure in the shallow mixed layer of the subtropical South China Sea reveals a peculiar copepod and zooplankton assemblage
Figure 3. Vertical variation of temperature (A), salinity (B) and chlorophyll a (C) of the six sampling stations.
Figure 6 in Autumn community structure in the shallow mixed layer of the subtropical South China Sea reveals a peculiar copepod and zooplankton assemblage
Figure 6. Clustering dendrogram of different samples using Bray–Curtis similarity and clustering strategy of flexible links in the northern South China Sea.
Figure 2 in Autumn community structure in the shallow mixed layer of the subtropical South China Sea reveals a peculiar copepod and zooplankton assemblage
Figure 2. Monthly averaged information derived from NOAA for sea surface temperature of September 1999.
Figure 1 in Autumn community structure in the shallow mixed layer of the subtropical South China Sea reveals a peculiar copepod and zooplankton assemblage
Figure 1. Map of sampling stations during Ocean Research Vessel III cruise 563, from 27 September to 2 October 1999. Station A6 is the South-East Asia Time Series (SEATS) station in the South China Sea.
Formation of the layered circulation in South China Sea with the mixing stimulated exchanging current through Luzon Strait
<p>Formation of the layered circulation in South China Sea with the mixing stimulated exchanging current through Luzon Strait</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>
ECCO Ocean Mixed Layer Depth - Daily Mean llc90 Grid (Version 4 Release 4)
This dataset provides daily-averaged ocean mixed layer depth on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Ocean Mixed Layer Depth - Daily Mean 0.5 Degree (Version 4 Release 4)
This dataset contains daily-averaged ocean mixed layer depth interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Ocean Mixed Layer Depth - Monthly Mean 0.5 Degree (Version 4 Release 4)
This dataset contains monthly-averaged ocean mixed layer depth interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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