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84 results for “ice temperature”
Data for: Low temperature ice nucleation of sea spray and secondary marine aerosols under cirrus cloud conditions
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Ice phenology and under-ice temperature and oxygen data for lakes of the Sierra Nevada, CA
<anchor id="_Hlk62995535"/>Warming winters will reduce ice cover and change under-ice conditions in temperate mountain lakes, where snow comprises most of winter cover on lakes. Snow-dominated mountain lakes are abundant and highly susceptible to climate warming, yet we lack an understanding of how climate variation and local attributes influence winter processes. We investigated climatic and intrinsic controls on ice phenology, water temperature, and bottom-water dissolved oxygen (DO) in 15 morphologically diverse lakes in the Sierra Nevada and Klamath mountains of California, USA, using high frequency measurements from multiple (2-5) winters. We found that ice phenology was determined by winter climate variables (snowfall, air temperature) that influence ice-off timing, whereas ice-on timing was relatively invariant among years. Lake size and morphology mediated the effect of climate on lake temperature and DO dynamics in early and late winter. Rates of hypolimnetic DO decline were highest in small, shallow lakes, and were unrelated to water temperature. Temperature and oxygen dynamics were more variable in small lakes because heavy snowfall caused ice submergence, mixing, and DO replenishment that affected the entire water column. As persistence of snow declines in temperate mountain regions, autumn and spring climatic conditions are expected to gain importance in regulating lake ice phenology. Water temperature and DO will likely increase in most lakes during winter as snowpack declines, but morphological attributes such as lake size will determine the sensitivity of ice phenology and under-ice processes to climate change.
Continuous snow temperature profiles from the Snow Ice Mass Balance Apparatus (SIMBA) (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), November 2022-June 2023
<p>Raw (Level 1) measurements from the Snow Ice Mass Balance Apparatus (SIMBA) deployed at the Avery Picnic site (~ 38°58.345' N, 106°59.811' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from November 2021 through June 2023. The SIMBA, originally designed for observing the mass balance of sea ice, is comprised of a thermistor chain with 2 cm spacing (Jackson et al., 2013). This system was configured for terrestrial snowpack by the manufacturer, SAMS Enterprise, to the specifications for SPLASH. The chain was installed suspended from a tripod and fixed to a rigid plastic bar near in time to the onset of snowpack in November 2022. The lowest 10 cm of the chain were buried within the soil. The top of the chain reached approximately 180 cm above the soil surface and snow was permitted to accumulate around the chain throughout the winter of 2022-2023. In the files, negative values of the "height" vector are below the soil surface and positive levels are above, which may be either snow or air depending on the snow depth. The system also uses a low-power heating cycle to measure thermistor's temperature response time for aiding in determining material interfaces: see Jackson et al. (2013) for details. </p><p>There are several cautions to be aware of when using these data. The data has been ingested into daily netCDF and metadata (in attributes) have been provided but no quality control has been carried out on this raw version of the data set. From 1 November through 22 December 2022, the sensor obtained profiles every 10 min after which corruption of the configuration file reverted the profiles to every 6 hours (0, 6, 12, and 18 UTC). After 1 January a problem in the firmware caused the system to lose connection to the time-synching GPS network and therefore the clock drifted from January through June 2023 (the maximum potential time stamping error is likely < 81 sec). Finally, from 23 March through 4 April 2023, the depth of the snow at the location of the sensor was deeper than 180 cm and thus measurements in the upper part of the snowpack were not observed then.</p><p>Jackson, K., J. Wilkinson, T. Maksym, D. Meldrum, J. Beckers, C. Haas, and D. Mackenzie (2013) A novel and low-cost sea ice mass balance buoy. Journal of Atmosphere and Oceanic Technology, 30(11), 2676-2688, https://doi.org/10.1175/JTECH-D-13-00058.1.</p>
Databases generated for Manuscript titled "Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures"
<p>Databases generated for manuscript "<strong>Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures</strong>"</p> <p>There are two zip files containing generated databases:</p> <p>The zip file titled "database.zip" contains generated database for calculated fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtb.csv (e.g. 000N000Etb.csv for 0°N, 0°E). Each .csv file contains four separate columns and variable rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to Thermal Emission Spectrometer (TES) measured temperature. "Tau" corresponds to calculated Dust visible opacities using the methodology presented in this work. The rows in each file vary based on the temporal observations from TES at each location. </p> <p>The zip file titled "fitdatabase.zip" contains generated database for fitted fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtbf.csv (e.g. 000N000Etbf.csv for 0°N, 0°E). Each .csv file contains six separate columns and three hundred and sixty rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to measured temperature. The fitting algorithm interpolates points between values in the calculated flux database and applies a rolling mean fit with a window spanning ten degrees in solar longitude centered at each calculated flux point. "FLAG" indicates the amount of points of calculated flux points that exist within the ten degree window centered at each flux point to demonstrate to the user how much data had to be fitted. "From Ls" shows the leftmost edge of the rolling mean fit window. "To Ls" shows the rightmost edge of the rolling mean fit window. The rows in each file correspond to one degree of solar longitude the fitting algorithm was designed to cover each solar longitude bin. </p> <p> </p>
Single-scattering properties of ice particles in the microwave regime: Temperature effect on the ice refractive index with implications in remote sensing
<p>This database is from the paper: Ding, J., L. Bi, P. Yang, G. W. Kattawar, F. Weng, Q. Liu, and T. Greenwald, 2017: Single-scattering properties of ice particles in the microwave regime: temperature effect on the ice refractive index with implications in remote sensing, Journal of Quantitative Spectroscopy & Radiative Transfer, 190, 26-37. DOI: 10.1016/j.jqsrt.2016.11.026.</p>
Record low Arctic sea ice extent in 2012 linked to two-year La Niña-driven sea surface temperature pattern
<p class="Abstract"><span>Arctic summer sea ice decline accelerated from the mid-2000s to 2012, with the 2012 record low remaining unbroken. While frequent La Niña events during this period have been suggested as a driver of this trend acceleration, no convincing evidence has been presented. Here, using a climate model nudged to observed pan-tropical sea surface temperatures (SST), we show that the back-to-back La Niña events during 2010–2011, followed by a North Pacific cooling and a marginal El Niño, were a key contribution to the 2012 record low. Specifically, the La Niña events in 2010–2011 warmed the Arctic Pacific sector, whereas tropical SST anomalies in 2012 strengthened the Greenland high pressure, leading to an Arctic dipole-like pressure pattern and strengthening of transpolar ice drift. These Arctic temperature and circulation anomalies led to the record low sea ice extent in 2012, highlighting the strong influence of tropical SSTs on Arctic climate.</span></p>
Observational data of temperature and oxygen for the study "High-frequency observations of temperature and dissolved oxygen reveal under-ice convection in a large lake"
<p>Matlab data for both temperature and oxygen time series between December 1, 2014, 0:00 (EST) and April 26, 2015, 0:00 (EST). Temperature profiles are sampled every 20 seconds. Dissolved oxygen are sampled every 30 minutes. The depth of each time series are written on the file name.</p>
Results of water temperature measurements and observations of ice cover occurrence on the Ropa and Biala rivers
<p>The dataset contains the results of water temperature measurements and ice cover occurrence on two rivers - the Ropa and the Biala. The measurements were carried out on December 29, 2021 under a research grant funded by the National Science Center of Poland (grant number: 2020/39/O/ST10/00652).</p>
Dataset: Infrared Spectroscopy and Quadrupole Mass Spectrometry during Temperature Programmed Desorption of Mixed Hyper-volatile (CO, N2, Ar) and Amorphous Ices (H2O, CO2)
<p>IR spectra of H2O:CO and CO2:CO ices mixed at 2 concentrations (5:1 and 15:1) and grown to 7 thicknesses (50 to 3000 ML). Spectra are also included for a smaller set of H2O:N2, H2O:Ar, CO2:N2, and CO2:Ar ices. </p> <p>The files are .txt files, where the first column is wavenumber (cm-1) and the second is IR absorbance.</p> <p>QMS data taken during TPD of H2O:CO and CO2:CO ices mixed at 2 concentrations (5:1 and 15:1) and grown to 7 thicknesses (50 to 3000 ML). Data are also included for a smaller set of H2O:N2, H2O:Ar, CO2:N2, and CO2:Ar ices.</p> <p>Constructing TPD curves require 2 files for each ice, a .asc file and a .xls file. The .asc files contain the relative time (s) and ion count for many relevant m/z. The .xls files contain time (s) and temperature (K). Time from each file can be interpolated to produce ion count as a function of temperature.</p>
Figure data for "Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds"
<p>Data supporting figures in the paper "Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds" published at <em>Communications Earth & Environment. </em></p>
Record low Arctic sea ice extent in 2012 linked to two-year La Niña-driven sea surface temperature pattern
Open the record for dataset details and reuse information.
Supporting model output for article "Relative Impact of Sea Ice and Temperature Changes on Arctic Marine Production"
<p>The following is a summary of processed model output data from a series of HiLAT model runs. A description of the model runs and visualization of model output and analysis can be found in the accompanying manuscript.</p> <p>Gibson, G.A., Weijer, W., Jeffery, N. and Wang, S (2020) Relative impact of sea ice and temperature changes on Arctic marine production. Journal of Geophysical Research: Biogeosciences. DOI: 10.1029/2019JG005343</p> <p>The model outputs and accompanying research was supported by the National Science Foundation 173886 and by DOE | SC |Biological and Environmental Research (BER).</p> <p>There are 4 versions of most output file types, corresponding to the 4 experiments that were conducted.</p> <p>These experiments are:</p> <p>i) the baseline model simulation (Base);</p> <p>ii) a moderate ice reduction experiment (S1);</p> <p>iii) a dramatic ice reduction experiment (S2); and</p> <p>iv) an atmospheric warming scenario (RCP45).</p> <p>Each of the 4 experiments is described in detail in Gibson et al (2020).</p> <p>Many variables in the output files are regional averages where model regions are indicated by a number *note - for aesthetics, the numbering used in the model output files differs slightly from the numbering used in the accompanying manuscript. </p> <p>Bering Sea =1</p> <p>Chukchi Sea =2 </p> <p>East Siberian Sea =3</p> <p>Laptev Sea =4 </p> <p>Beaufort Sea =5</p> <p>Barents Sea =6</p> <p>Canadian =7 </p> <p>Eurasian Basins =8 </p> <p>Nordic Seas =9</p> <p>Labrador Sea=10</p> <p>Kara Sea =11</p> <p> </p> <p><strong>Grid </strong></p> <p>gx1v6_polar_mask.10.12.18.nc This file is the original grid file used for all model runs with the addition of Arctic_Region and Antarctic_Polar_Front variables which indicates which grid cells are in the model regions described above.</p> <p> </p> <p><strong>Forcing</strong></p> <p>ncep_RCP45 ncep.t_10.T62.*.nc where * is 1948-2009 These files contain the modified atmospheric forcing used for the RCP45 model experiment. The unmodified NCEP forcing is freely publically available elsewhere to not duplicated here</p> <p> </p> <p><strong>Ice </strong></p> <p>Ice-Regional-Monthly-177-186-*.nc where * indicates model run - Base, S1, S2 or RCP45. These file contain the monthly averaged ice variables (ice area, ice algae production) for each model region for the last ten years of the model run.</p> <p> </p> <p>cice-monthly-177-186-*.nc where * indicates model run - Base, S1, S2 or RCP45. These file are the monthly averaged output filed from the cice model. Variables are averaged over each model analysis region.</p> <p> </p> <p>Ice_snow_depth_region_Avg-137-186-*.nc where * indicates model run - Base, S1, S2 or RCP45. These file contain the regionally averaged ice and snow depth for each analysis region.</p> <p> </p> <p>IceVol_Base-137-186.nc Contains ice variable file for the baseline model run. Model contains ice variables hi and aice for every grid cell of the model domain. Ice volume was computed as hi * Tarea (from grid file).</p> <p> </p> <p>Annual-SummerAvg-IceArea-177-186_*.nc Where * indicates model run - Base, S1, S2 or RCP45. Each of these files contains the annual average ice area averaged over Summer months (JuneAugust) for last 10 years of model run (200-2009) for each grid cell in the HiLAT model</p> <p> </p> <p><strong>Ocean variables </strong></p> <p>TempNitrate TEMP_NO-177-186.nc This file contains model estimates of temperature and nitrate for the baseline model run for model years 177-186 (2000-2009, the last en years of the model run) for the upper five model layers (500, 1500, 2500, 3500, 4500 cm).</p> <p> </p> <p><strong>CO2flux </strong></p> <p>AnnAvgAllCflux*.nc</p> <p>Where * indicates model run - Base, S1, S2 or RCP45. Each of these files contains the annual average CO2 Flux for last 10 years of model run (200-2009) for each grid cell of the HiLAT model.</p> <p> </p> <p>CO2flux_region_avg-177-186-*.nc Where * indicates model run - Base, S1, S2 or RCP45. Each of these files contains the annual average CO2 Flux for last 10 years of model run (2000-2009) for each region of the HiLAT model.</p> <p> </p> <p><strong>Ocean Production </strong></p> <p>OceanProd_Year-147-156-*.nc OceanProd_Year-157-166-*.nc OceanProd_Year-167-176-*.nc OceanProd_Year-177-186-*.nc Where * indicates model run - Base, S1, S2 or RCP45. These file contain the annual average vertically integrated ocean phytoplankton production for each grid cell. Each file contains 10 model years is the sum of diatoms, diazotrophs, phaeocystis, and smaller phytoplankton.</p> <p>Region_production_by_phyto_group-diat_ps-137-186-*.nc Where * indicates model run - Base, S1, S2 or RCP45. These file contain the annual average vertically integrated ocean phytoplankton production for each region. Each file contains group specific production for diatoms, diazotrophs, phaeocystis, and smaller phytoplankton.</p> <p>OceanProd_Daily-177-186-BASE.nc This file contains the daily average vertically integrated ocean phytoplankton production for each grid cell. The file contains 10 model years. Production is the sum of all four phytoplankton groups - diatoms, diazotrophs, phaeocystis, and smaller phytoplankton.</p> <p> </p> <p><strong>PAR </strong></p> <p>PAR-MonthyAvg-177-168-*.nc</p> <p>Photosynthetically Active Radiation in the ocean. Where * indicates model run - Base, S1, S2 or RCP45.</p>
Chongce ice core stable isotopes and temperature reconstruction (1524-2012 A.D.)
<p>Chongce ice core stable isotopes and temperature reconstruction</p>
Data for "Antarctic ice-sheet meltwater reduces transient warming and climate sensitivity through the sea-surface temperature pattern effect"
<p>Data of the Historical Hosing simulations presented in "Antarctic ice-sheet meltwater reduces transient warming and climate sensitivity through the sea-surface temperature pattern effect" submitted to Geophysical Research Letters</p> <p>Authors: Yue Dong, Andrew G. Pauling, Shaina Sadai, Kyle C. Armour </p> <p>Abstract:</p> <p>Coupled global climate models (GCMs) generally fail to reproduce the observed sea-surface temperature (SST) trend pattern since the 1980s. The model-observation discrepancies may arise in part from the lack of realistic Antarctic ice-sheet meltwater imbalance in GCMs. Here we employ two sets of CESM1-CAM5 simulations forced by anomalous Antarctic meltwater fluxes over 1980--2013 and into the 21st century. Both show a reduced global warming rate and an SST trend pattern that better resembles observations. The meltwater drives surface cooling in the Southern Ocean and the tropical southeast Pacific, in turn increasing low-cloud cover and driving radiative feedbacks to become more stabilizing (corresponding to a lower effective climate sensitivity). These feedback changes contribute more than ocean heat uptake efficiency changes in reducing the global warming rate. Accurately projecting historical and future warming thus requires improved representation of Antarctic meltwater and its impacts in models. </p>
Experiments on the snowfall, temperature, and humidity to the Arctic summer snowstorm using ocean-ice couple model (POP2-CICE5) with JRA55-do and MERRA2 forcing
<p><span>In the Arctic, short-lived summer snowstorms can provide snow cover that can increase surface reflectivity and heat capacity. Despite their potential importance, little research has been done to understand the impact of summer snowstorms on basin-scale Arctic sea ice cover. Our observational analysis shows that a summer snowstorm event is accompanied by cyclonic ice drift, increases in surface albedo and surface air cooling that can persist for up to ~2 weeks, dampening sea ice loss. Specifically, multiple snowstorm events in a summer, on average, results in net increase in sea ice extent of ~0.2×106 km<sup>2</sup> by early September. Experiments with a sophisticated ice-ocean model framework indicate that the initial expansion of sea ice extent is driven by cyclonic wind-driven ice drifts driving sea ice southwards and increasing albedo around the summer ice edge, however the thermal effects from the associated snowfall and atmospheric conditions result in a stronger overall impact on basin-averaged sea ice extent at seasonal scales.</span></p> <p><span>Additional model experiments were carried out to isolate the physical processes contributing to the thermal response of Arctic sea ice to summer snowstorms. Our results show the impact of surface air cooling on sea ice extent is about 3.5 times larger than the snowfall/albedo response. However, our simulated albedo response is weaker than the observed response, likely due to the negligible difference in surface albedo between old snow and freshly fallen snow – a limiting factor in our analysis and a topic worthy of future focus.</span></p>
Experiments on the snowfall, temperature, and humidity to the Arctic summer snowstorm using ocean-ice couple model (POP2-CICE5) with JRA55-do and MERRA2 forcing
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Meltpond2000 Polarimetric Scanning Radiometer Sea Ice Brightness Temperatures, Version 1
The Meltpond2000 project was the first in a series of Arctic and Antarctic aircraft campaigns to validate sea ice algorithms developed for the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), primarily to quantify errors in AMSR-E sea ice products resulting from the presence of melt ponds.
Snow Melt Onset Over Arctic Sea Ice from SMMR and SSM/I-SSMIS Brightness Temperatures, Version 5
This data set includes yearly snow melt onset dates over Arctic sea ice derived from Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS) brightness temperature measurements. The data are gridded to the 25 km Northern Hemisphere Polar Stereographic projection and available from 1979 through 2022. One browse image is available for each year. This data set also contains value-added statistics for each grid cell, including: mean melt onset date, latest (maximum) melt onset date, earliest (minimum) melt onset date, range of melt onset dates (the difference between maximum and minimum onset dates), and the standard deviation of melt onset dates. One browse image is also provided for each statistical field.
NRT AMSR2 Unified L3 Daily 25 km Brightness Temperature & Sea Ice Concentration Polar Grids V4
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The NRT AMSR2 Unified L3 Daily 25 km Brightness Temperature & Sea Ice Concentration Polar Grids, Version 4 uses as input the resampled brightness temperature (Level-1R) data provided by the Japanese Aerospace Exploration Agency (JAXA). The Version 4 dataset uses the AMSR-U2 product generation algorithm with slight modifications for NRT product generation, same algorithm used to generation the standard, science quality, data that is available at the NSIDC DAAC. This Level-3 gridded product includes brightness temperatures at 6.9 through 89.0 GHz and sea ice concentrations. Data are mapped to a polar stereographic grid at 25 km spatial resolution. Sea ice concentration and brightness temperatures include daily ascending averages, daily descending averages, and daily averages. Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/seaice25. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science. These standard product, science quality, are available at the NSIDC DAAC: https://nsidc.org/
NRT AMSR2 Unified L3 Daily 12.5 km Brightness Temperature & Sea Ice Concentration V4
The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on the Global Change Observation Mission - Water 1 (GCOM-W1) provides global passive microwave measurements of terrestrial, oceanic, and atmospheric parameters for the investigation of global water and energy cycles. Near real-time (NRT) products are generated within 3 hours of the last observations in the file, by the Land Atmosphere Near real-time Capability for EOS (LANCE) at the AMSR Science Investigator-led Processing System (AMSR SIPS), which is collocated with the Global Hydrology Resource Center (GHRC) DAAC. The NRT AMSR2 Unified L3 Daily 12.5 km Brightness Temperature & Sea Ice Concentration, Version 4 uses as input the resampled brightness temperature (Level-1R) data provided by the Japanese Aerospace Exploration Agency (JAXA). The Version 4 dataset uses the AMSR-U2 product generation algorithm with slight modifications for NRT product generation, same algorithm used to generation the standard, science quality, data that is available at the NSIDC DAAC. This Level-3 gridded product includes brightness temperatures at 89.0 GHz. Data are mapped to a polar stereographic grid at 12.5 km spatial resolution. Sea ice concentration and brightness temperatures include daily ascending averages, daily descending averages, and daily averages. Data are stored in HDF-EOS5 format and are available via HTTP from the EOSDIS LANCE system at https://lance.nsstc.nasa.gov/amsr2-science/data/level3/seaice12. If data latency is not a primary concern, please consider using science quality products. Science products are created using the best available ancillary, calibration and ephemeris information. Science quality products are an internally consistent, well-calibrated record of the Earth's geophysical properties to support science. These standard product, science quality, are available at the NSIDC DAAC: https://nsidc.org/
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.