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1,574 results for “atmospheres”
Global atmospheric simulation using the Super-Parameterized Community Atmosphere Model
<p>A 1-month subset from a global atmospheric simulation using the Super-Parameterized Community Atmosphere Model (SP-CAM), which implements the Multi-scale Modeling Framework (MMF) described in Khairoutdinov and Randall (2001) and Khairoutdinov et al. (2005). The version of the SP-CAM used here is described in Marchand et al. (2009) and Ovtchinnikov et al (2006), and was run at Pacific Northwest National Laboratory with DOE support. It is based on CAM 3.0 for the global atmospheric component, and uses the System for Atmospheric Modeling (SAM; Khairoutdinov and Randall 2003). This simulation is configured with CAM running the finite volume dynamical core on a 2x2.5 degree latitude-longitude grid with 26 vertical levels. The embedded CRM (SAM) is configured with 64 horizontal columns at 4 km grid spacing with 24 vertical levels (sharing the bottom 24 levels with the CAM grid), and single-moment microphysics. The simulation was initialized on 1 September 1997 and runs through June 2002, forced with observed monthly-mean sea surface temperatures. Only the month of July 2000 is uploaded here, which is what is required to reproduce the results in Hillman et al. (2018).</p>
Atmospheric climate model output of the COSMO-CLM2 regional climate model hindcast run over Antarctica (1987-2016)
<p>The dataset contains monthly output of a COSMO-CLM² (COSMO-CLM coupled to the Community Land Model) atmospheric hindcast simulation over Antarctica which is described and evaluated in the following paper: </p> <p>Souverijns, N., Gossart, A., Demuzere, M., Lenaerts, J.T.M., Medley, B., Gorodetskaya, I.V., Vanden Broucke, S., van Lipzig, N.P.M., 2019. A new Regional Climate Model for POLAR-CORDEX: Evaluation of a 30-year Hindcast with COSMO-CLM² over Antarctica. Journal of Geophysical Research: Atmospheres, 124, 1405-1427. (doi:10.1029/2018JD028862)</p> <p>Details of the model simulation:<br> - COSMO-CLM version 5.0_clm6<br> - Community Land Model version 4.5<br> - Horizontal resolution: 0.25°x0.25°<br> - Vertical resolution: 40 levels<br> - Time period: 1987-2016 (excluding 4 years of spin-up)<br> - Driving model: ERA-Interim<br> </p> <p>The data provided here has a monthly time resolution and contains the monthly average of all variables except denoted otherwise below. As such, each file consists of 360 time steps.<br> - AEVAP_S: Surface evaporation [kg m-2] (summed value for each month)<br> - ALB: Surface albedo [-] (only for austral summer months)<br> - ALHFL_S: Surface latent heat flux [W m-2]<br> - ALWD_S: Downward longwave radiation at the surface [W m-2]<br> - ALWU_S: Upward longwave radiation at the surface [W m-2]<br> - ASHFL_S: Surface sensible heat flux [W m-2]<br> - ASOB_S: Surface net downward shortwave radiation [W m-2]<br> - ASWDIFD_S: Diffuse downward shortwave radiation at the surface [W m-2]<br> - ASWDIFU_S: Diffuse upward shortwave radiation at the surface [W m-2]<br> - ASWDIR_S: Direct downward shortwave radiation at the surface [W m-2]<br> - ATHB_S: Surface net downward longwave radiation at the surface [W m-2]<br> - P: Pressure at 40 vertical levels [Pa]<br> - QV: Specific humidity at 40 vertical levels [kg kg-1]<br> - RH2M: Relative humidity at 2 meter [%]<br> - SNOW_GSP: Surface snowfall amount [kg m-2] (summed value for each month)<br> - T2M: Temperature at 2 meter [K]<br> - T: Temperature at 40 vertical levels [K]<br> - WS10M: Wind speed at 10 meter [m s-1]<br> - WS: Wind speed at 40 vertical levels [m s-1]</p>
Emissions Database for Global Atmospheric Research, version v4.3.2 part I Greenhouse gases
<p>The Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2, partim Greenhouse gases compiles anthropogenic emissions data for CO2, CH4 and N2O based on international statistics and emission factors. The version v4.3.2 of the EDGAR emission inventory provides global estimates, broken down to IPCC-relevant source-sector levels, from 1970 (the year of EU’s first Air Quality Directive) to 2012 (the end year of the first commitment period of the Kyoto Protocol (KP)). Strengths of EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time, and comprehensiveness in activities. Emission sources of the multiple gases include all human activities except the land-use, land-use change and forestry sector and are compiled following a bottom-up and IPCC-compliant approach. The dataset provides in addition to the complete timeseries 1970-2012 also annual and global gridmaps of 0.1 degree by 0.1 degree resolution for each source-sector and each year. For 2010 also 12 monthly gridmaps per source-sector are provided.</p>
Radar and Lidar scattering lookup tables for atmospheric hydrometeors using a T-Matrix method and a Mie theory
<h2>Overview</h2> <p>The database includes text files containing the scattering amplitude matrices for single spherical/nonspherical particles for radar and lidar. They are the lookup tables used for calculating radar and lidar observables in the Cloud-Resolving Radar Simulator (Oue et al. 2020). The radar scattering properties were calculated for several hydrometeor categories using a T-matrix method proposed by Mishchenko (2000) accounting for incident angles, scattering direction (forward and backward), polarimetry (horizontally (H) and vertically (V) polarized waves), particle aspect ratio, phase (liquid or ice), bulk density, temperature, particle size, and radar frequency. The lidar scattering properties at a vertical incidence were calculated for spherical liquid or ice particles using the BHMIE Mie code (Bohrean and Hyffman,1998) accounting for lidar wavelength, temperature, and bulk density. The hydrometeor categories are commonly used for cloud resolving models employing bulk microphysical schemes (e.g., cloud, rain, ice cloud, snow aggregates, and graupel). Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v3.34).</p> <h2>Data structure</h2> <p>The data files are arranged and zipped every hydrometeor types. The names of the tar-zipped directories under the top directory LLUT3 represents the hydrometer type.<br>For lidar scattering, the following directories are included:<br>ceilo: Ceilometer lidar backscatter properties at a wavelength of 905 nm<br>mpl: Micropulse lidar (MPL) backscatter properties at wavelengths of 353 and 532 nm</p> <p>For radar scattering, the following hydrometer types are included:<br>cloud: Radar scattering for liquid cloud droplets (spherical shape)<br>raina: Radar scattering for raindrops with the aspect ratio model proposed by Andsager et al. (1999)<br>rainb: Radar scattering for raindrops with the aspect ratio model proposed by Brandes et al (2002)<br>ice_ar0.90: Radar scattering for cloud ice with an aspect ratio of 0.9<br>ice_ar0.20: Radar scattering for cloud ice with an aspect ratio of 0.2<br>smallice: Radar scattering for spherical cloud ice particles<br>snow_ar0.60: Radar scattering for snowflakes with an aspect ratio of 0.6<br>graupel_ar0.60: Radar scattering for graupel particles with an aspect ratio of 0.6<br>graupel_ar0.80: Radar scattering for graupel particles with an aspect ratio of 0.8<br>graupel: Radar scattering for spherical graupel particles<br>gh_ryzh: Radar scattering for graupel particles with the graupel aspect ratio model proposed by Ryzhkov et al (2011)<br>unrimedice_ar0.40: Radar scattering for unrimed ice particles with an aspect ratio of 0.4<br>unrimedice_ar0.60: Radar scattering for unrimed ice particles with an aspect ratio of 0.6<br>unrimedice_ar0.80: Radar scattering for unrimed ice particles with an aspect ratio of 0.8<br>unrimedice: Radar scattering for spherical unrimed ice particles<br>partrimedice_ar0.40: Radar scattering for partially rimed ice particles with an aspect ratio of 0.4<br>partrimedice_ar0.60: Radar scattering for partially rimed ice particles with an aspect ratio of 0.6<br>partrimedice_ar0.80: Radar scattering for partially rimed ice particles with an aspect ratio of 0.8<br>partrimedice: Radar scattering for partially rimed spherical ice particles </p> <h2>The file name convention </h2> <p>For lidar scattering data, each file name has the following format:<br>[hydrometeor type]_[instrument name]_ [wavelength in nm]_[phase ID]_d[bulk density in kg m-3].dat<br>The hydrometeor type shows: 1) ‘cld’ for liquid cloud droplets, and 2) ‘ice’ for ice particles. The phase ID shows: 1) ‘p25’ for ceilometer liquid cloud, 2) ‘p20’ for MPL lidar liquid cloud, and 3) ‘m30’ for MPL lidar ice. </p> <p>For radar scattering data, each file name has the following format.<br>[hydrometeor type]_fr[frequency in GHz]GHz_t[temperature in K]_rho[bulk density in kg m-3]_el[elevation angle in degree].dat<br>The hydrometeor type follows the directory name presented above.</p> <h2>Format of the data files</h2> <p>Line 1: Wavelength in mm<br>Line 2: Temperature in K<br>Line 3: Refractive index (real and imaginary)<br>Line 4: Number of radii calculated and number of elevation angles<br>Line 6: Incident angle and scattered angle in degrees<br>Line 7: Radius in mm and aspect ratio<br>Line 8: Forward scattering amplitude for co-polarization VV and HH (complex number)<br>Line 9: Backward scattering amplitude for co- and cross polarizations VV, VH, HV, HH (complex number) <br>Line 10 to the end of file: Repeat Line 7 to Line 9 with different radii until the maximum radius.</p>
Data supporting the study "An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021))
<p>Data supporting the figures and findings presented in the study <strong>"An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021), <em>Atmos. Chem. Phys..</em></strong></p>
Daily Anomalies of the Surface Atmospheric Fluxes of the Brazilian Northeast (DASAF-BNE)
<p>This dataset contains the daily anomalies of the main atmospheric fluxes throughout the Brazilian NE region. Geographically it is framed at 42.5°W - 29.75°E/20.5°S - 1.25°N, in the time range from 1979-01-01 12:00:00 to 2017-12-31 12:00:00. The DASAF-BNE dataset with a spatial resolution of 1 degree was the basis for the calculation of the daily anomalies, they were interpolated by the bilinear method to obtain a resolution of 0.25 degrees.</p>
Atmosphere-cryosphere interactions during the last phase of the LGM (21 ka BP) in the European Alps
<p>This dataset refers to: Del Gobbo, C., Colucci, R. R., Monegato, G., Žebre, M., and Giorgi, F.: Atmosphere-cryosphere interactions at 21 ka BP in the European Alps, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-43, in review, 2022. </p> <p> </p> <p>We used the regional climate model RegCM4 to investigate the physical processes sustaining the glacier extent during the Last Glacial Maximum (LGM) and pre-industrial time (PI) over the European Alps. After a bias-correction of precipitation and temperature data, we reconstructed the environmental equilibrium line altitude (envELA) of the Alpine glaciers, which resulted consistent with geological records. </p> <p>#----------------------------------------------------------</p> <p> </p> <p>LGM in the file names referes to 21 ka BP</p> <p>PI refers to pre-industrial</p> <p>#----------------------------------------------------------</p> <p> </p> <p><strong>This dataset contains:</strong></p> <p><strong>NetCDF files ------------------------------------------------------------------------------------</strong></p> <p> </p> <ul> <li><strong>Monthly mean TAS and PR</strong> <ul> <li>variables = <ul> <li>RegCM4 monthly mean near-surface air temperature (TAS)</li> <li>RegCM4 monthly mean precipitation (PR)</li> <li>model topography (topo)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method = RCM forced with MPI-ESM-P</li> <li>remapped = no</li> <li>resolution = 12 km</li> <li>files = <ul> <li>LGM_PR_TAS_monmean.nc</li> <li>PI_PR_TAS_monmean.nc</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Bias-corrected monthly mean TAS and PR</strong> <ul> <li>variables = <ul> <li>model topography (topo)</li> <li>Bias-corrected RegCM4 monthly mean precipitation (PR)</li> <li>Bias-corrected RegCM4 monthly mean near-surface air temperature (TAS)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method = bias-correction based on HISTALP (TAS) and LAPrec (PR) of RegCM4 data</li> <li>remapped = onto HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files= <ul> <li>LGM_PR_TAS_monmean_BC.nc</li> <li>PI_PR_TAS_monmean_BC.nc</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>ELA</strong> <ul> <li>variables = <ul> <li>ELA </li> <li>average RegCM-HISTALP-LAPrec topography</li> </ul> </li> <li>units = m a.s.l.</li> <li>data = calculated from bias-corrected RegCM4 data</li> <li>method = Zebre et al. (2020)</li> <li>remapped = on HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files = <ul> <li>LGM_ELA.nc</li> <li>PI_ELA.nc</li> </ul> </li> </ul> </li> </ul> <p><br> <strong>csv files ------------------------------------------------------------------------------------</strong></p> <p><strong>* dates refer to model dates, not real ones!!!</strong><br> tj_700_hpa_pr_lgm : Tagliamento glacier daily wind and precipitation at the 21 ka BP<br> tj_700_hpa_pr_pi : Tagliamento glacier daily wind and precipitation at the PI<br> db_700_hpa_pr_lgm : Dora Baltea glacier daily wind and precipitation at 21 ka BP<br> db_700_hpa_pr_pi : Dora Baltea glacier daily wind and precipitation at the PI<br> r_700_hpa_pr_lgm : Rhine glacier daily wind and precipitation at 21 ka BP<br> r_700_hpa_pr_pi : Rhine glacier daily wind and precipitation at the PI<br> ist_700_hpa_pr_lgm : Inn-Salzach-Traun glacier daily wind and precipitation at 21 ka BP<br> ist_700_hpa_pr_pi : Inn-Salzach-Traun glacier daily wind and precipitation at the PI</p> <p> </p>
CESM2 Idealized Experiment Output: Summer atmospheric response to zero May North American snow cover
<p>The National Center for Atmospheric Research’s Community Earth System Model version 2.2 (CESM2) (Danabasoglu et al., 2020) was run in the Atmospheric Model Intercomparison Project (AMIP) configuration. SSTs and sea-ice were prescribed as monthly varying seasonal cycles based on the observed climatology from 2005 to 2015 (i.e., component set: F2010climo) (Hurrell et al., 2008). We employed the Community Atmosphere Model version 6 (CAM6) (Bogenschutz et al., 2018)<span> </span>as the atmospheric component and the Community Land Model version 5 (CLM5) (Lawrence et al., 2019) as the land-surface component. Each model was run with a horizontal resolution of 0.9˚ latitude by 1.25˚ longitude.</p> <p>We ran a control simulation in this configuration for ten consecutive years. We then modified the land-surface restart files for May 1st of each year by reducing the snow cover over North America to zero. Using these modified files, we then completed a reduced snow simulation by rerunning three-month simulations from May through July for each of the ten years. </p>
Atmospheric Effects on Neutron Star Parameter Constraints with NICER
<p>Posterior sample files associated with the publication "Atmospheric Effects on Neutron Star Parameter Constraints with NICER" by Salmi et al. (2023; <a href="https://doi.org/10.48550/arXiv.2308.09319">arXiv:2308.09319</a>; <a href="https://doi.org/10.3847/1538-4357/acf49d">https://doi.org/10.3847/1538-4357/acf49d</a>).</p><p>Also included are: the data products; the numeric model files including the telescope calibration products; model modules in the Python language using the X-PSI framework; and Jupyter analysis notebooks.</p><p>Please refer to the README for detailed information.</p>
Accessible Oceans: Auditory Display. Net flux of CO2 between Ocean and Atmosphere
<p>The seven tracks make up an auditory display of the net flux of carbon dioxide between the ocean and the atmosphere. The seven tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/RhkRBKbRTueE2F86b5QS">listen online here</a>.</p> <p>The data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the OOI Nugget developed by Dr. Leslie Smith. (<a href="https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/">https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/</a>)</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Atmospheric Wet Deposition in Urban and Suburban Sites Across the United States
These data are for the publication: Conrad-Rooney, E., J. Gewirtzman, Y. Pappas, V.J. Pasquarella, L.R. Hutyra, and P.H. Templer. 2023. Atmospheric Wet Deposition in Urban and Suburban Sites Across the United States. Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2023.119783 This study investigated long-term trends in atmospheric wet deposition of nitrogen (ammonium and nitrate), sulfate, cations, and chloride for urban and suburban sites in the U.S. using data from the National Atmospheric Deposition Program and assessed whether urban NADP sites are hotspots for atmospheric wet deposition. To examine potential impacts of urbanization on atmospheric wet deposition, percent impervious surface area and population density data were extracted from Google Earth Engine. The results of this study highlight that urban areas have greater rates of many forms of atmospheric deposition and that there should be more long-term monitoring of atmospheric deposition in cities and suburban sites throughout the U.S. Data sources: - Dewitz, J., and U.S. Geological Survey, 2021, National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021): U.S. Geological Survey data release, doi:10.5066/P9KZCM54 - National Atmospheric Deposition Program (NRSP-3). 2022. NADP Program Office, Wisconsin State Laboratory of Hygiene, 465 Henry Mall, Madison, WI 53706. https://nadp.slh.wisc.edu/networks/national-trends-network/ - United States Census Bureau, TIGER: US Census Blocks, 2010 United States Census. https://developers.google.com/earth-engine/datasets/catalog/TIGER_2010_Blocks#description
Marcell Experimental Forest daily bulk atmospheric deposition, 2009 - ongoing
This data set reports daily bulk atmospheric deposition calculated from the event-based chemistry of precipitation water that was collected at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota. The data come from sites in two research catchments instrumented for hydrologic monitoring - the meteorological station located in an upland clearing in the S2 research catchment and the S1 bog as part of the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. The MEF is operated and maintained by the USDA Forest Service, Northern Research Station. The SPRUCE experiment is a multi-year cooperative project among scientists of the Oak Ridge National Laboratory operated by UT-Battelle, LLC and the USDA Forest Service, Northern Research Station. The SPRUCE experiment is funded by the US Department of Energy, Biological and Environmental Research Program.
Baltimore Ecosystem Study: Soil atmosphere fluxes of carbon dioxide, nitrous oxide and methane, 1998 - ongoing
The Baltimore Ecosystem Study (BES) established a network of long-term permanent biogeochemical study plots in 1998. These plots provide long-term data on vegetation, soil and hydrologic processes in the key ecosystem types within the urban ecosystem. The network of study plots includes forest plots (upland and riparian), chosen to represent the range of forest conditions in the area and grass plots (to represent home lawns). Plots are instrumented with lysimeters (drainage and tension) to sample soil solution chemistry, time domain reflectometry probes to measure soil moisture, dataloggers to measure and record soil temperature, and trace gas flux chambers to measure the flux of carbon dioxide, nitrous oxide and methane from soil to the atmosphere. Measurements of in situ nitrogen mineralization, nitrification and denitrification were made at approximately monthly intervals from Fall 1998 - Fall 2000. Detailed vegetation characterization (all layers) was done in summer 1998 and 2015. Data from these plots has been published in Groffman et al. (2006, 2009), Groffman and Pouyat (2009), Savva et al. (2010), Costa and Groffman (2013), Duncan et al. (2013), Waters et al. (2014), Ni and Groffman (2018), Templeton et al. (2019). Literature Cited Costa, K.H. and P.M. Groffman. 2013. Factors regulating net methane flux in urban forests and grasslands. Soil Science Society of America Journal 77:850 - 855. Duncan, J. M., L. E. Band, and P. M. Groffman. 2013. Towards closing the watershed nitrogen budget: Spatial and temporal scaling of denitrification. Journal of Geophysical Research Biogeosciences 118:1-5; DOI: 10.1002/jgrg.20090 Groffman PM, Pouyat RV, Cadenasso ML, Zipperer WC, Szlavecz K, Yesilonis IC,. Band LE and Brush GS. 2006. Land use context and natural soil controls on plant community composition and soil nitrogen and carbon dynamics in urban and rural forests. Forest Ecology and Management 236:177-192. Groffman, P.M., C.O. Williams, R.V. Pouyat, L.E. Band and I.C.
Long-term monitoring of wet, dry, and bulk atmospheric deposition in central Arizona-Phoenix, ongoing since 1999
The aims of this study are to examine (1) the magnitude and spatial variability in the concentration and flux of wet deposited major ions (NO3-N, NH4-N, DOC, PO4-P, Cl, SO4, H+, Ca, Mg, Na, K) across the greater Phoenix metropolitan area, including the developed urban core and outlying desert, and (2) patterns of coarse dry particulate deposition across stated area and provide some minimum estimates on levels of dry deposition of these ions. This study was designed particularly to answer the question: 'To what extent are concentrations and fluxes of these ions enhanced at sites within the urban core relative to undeveloped desert sites upwind and downwind of the city?'. At the outset, the project featured eight wet-dry collectors positioned spatially so as to form a transect running approximately west-to-east across the central Arizona region from outlying desert to the west, upwind of the prevailing synoptic wind direction, through agriculture to urban core sites, and, finally, to two downwind sites in the desert to the east and northeast. As much as possible, these collectors were co-located with Maricopa County or Arizona Department of Environmental Quality monitoring stations. Monitoring at most sampling locations ran from 1999 through the mid-2000s when sampling was discontinued at several sites. Sampling continued at the Lost Dutchman State Park, also a Desert Fertilization experiment site with a focus on atmospheric deposition, through 2016. Sampling continues at a site on the Arizona State University Tempe campus that was added to the program in 2009.
Hubbard Brook Experimental Forest: Soil-atmosphere fluxes of carbon dioxide, nitrous oxide and methane on Watershed 1 and Bear Brook, 2002-2024
Soil atmosphere fluxes of the trace gases; carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) have been measured at several locations at the Hubbard Brook Experimental Forest (HBEF) including 1) the "freeze" study reference plots that provide contrast between stands dominated (80%) by sugar maple versus yellow birch and low and high elevation areas, 2) the Bear Brook Watershed where trace gas sampling is coordinated with long-term monitoring of microbial biomass and activity and 3) watershed 1 where trace gas sampling locations were co-located with long-term microbial biomass and activity monitoring sites that are located near a subset of the lysimeter sites established for the calcium addition study on this watershed. This dataset contains the Watershed 1 and Bear Brook data. Freeze plot trace gas can be found in: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=251. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Atmospheric deposition chemistry data from dryfall at the Jornada Basin LTER: 1983-ongoing
This data package contains concentrations of water soluble components from dryfall (dust) atmospheric deposition collected at the Jornada Basin LTER weather station north of Las Cruces, NM in Dona Ana County, New Mexico, USA. Atmospheric deposition as found in dryfall (dust) and wetfall precipitation has been collected at this location since 1983 using an Aerochem Metrics wetfall/dryfall collector. Dryfall occurring as atmospheric fallout is collected monthly. Each sample is analyzed for Br, Ca, Cl, F, HPO4, K, Mg, Na, NH4, NO3/NO2, SO4, Total N, and Total P. Analysis of Sr and Dissolved Organic Nitrogen was discontinued in 2003. Wetfall precipitation chemistry is available in data package knb-lter-jrn.210128002.
Atmospheric deposition chemistry data from wetfall at the Jornada Basin LTER: 1983-ongoing
This ongoing data package contains concentrations of water soluble components in wetfall (precipitation) atmospheric deposition collected at the Jornada Basin LTER weather station north of Las Cruces, NM in Dona Ana County, New Mexico, USA. Atmospheric deposition as found in dryfall (dust) and wetfall precipitation has been collected at this location since 1983 using an Aerochem Metrics wetfall/dryfall collector. Wetfall occurring as precipitation is collected after each event with a sample size large enough to analyze. Each sample is analyzed for Br, Ca, Cl, F, HPO4, K, Mg, Na, NH4, NO3/NO2, SO4, Total N, and Total P. Analysis of Sr and Dissolved Organic Nitrogen was discontinued in 2003. Dryfall atmospheric deposition chemistry data is available in data package knb-lter-jrn.210128001.
ANA01 Weekly, seasonal and annual measurement of precipitation volume and chemistry collected as part of the National Atmospheric Deposition Program at Konza Prairie
Data set contains results of chemical analysis of wetfall samples collected on Konza Prairie. Analysis is done by the Central Analytical Lab (CAL), Champaign, IL as part of the National Atmospheric Deposition Program (NADP). NADP data products available on the NADP/NTN web site (nadp.slh.wisc.edu/data/NTN/) include: Annual Data Summaries, Semiannual Data Reports, Annual and Seasonal Averages, Monthly Averages, and Weekly data. Konza Prairie LTER archives and provides the weekly data in electronic form before May 2019.
Luquillo Experimental Forest atmospheric and high and mid elevation weather data.
The data archive is here:https://doi.org/10.2737/RDS-2022-0050 please use this DOI when citing this dataset. This data publication contains daily means from laser ceilometer data from Sabana, ozone data from Bisley, and meteorological data collected from Bisley and the mountain top station of East Peak, all located on the Luquillo Experimental Forest (El Yunque National Forest) in Puerto Rico. Atmospheric data include: mean cloud observed frequency, mean lowest height cloud (cloud base), mean daytime mixing layer height observed frequency, mean lowest mixing layer height from hours 7am and 7pm only, and mean daytime mixing layer height collected from February 2013 through April 2021. Also included is mean ozone amount collected from April 2008 through early April 2021. The cloud and mixing layer frequency and low values were calculated using the Automated Surface Observing System (ASOS) method employed at airports in the area. Weather data include high elevation East Peak mean northeast wind speed (wind rose quartiles 45° to 90°), mean southeast wind speed (wind rose quartiles 90° to 135°), and mean total wind speed measured from October 2009 through 2020 (and a few months in 2021). Additional weather data collected from January 2008 through mid October 2020 include mid elevation Bisley mean precipitation, mean relative humidity, and mean temperature. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Estimate of the atmospherically-forced contribution to sea surface height variability based on altimetric observations
<p>This repository contains the estimate of the atmospherically-forced contribution to sea level variability described in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>, and derived from the Ssalto/Duacs altimeter products produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (<a href="http://www.marine.copernicus.eu">http://www.marine.copernicus.eu</a>).</p> <p>The files contain successive 5-day averages of sea level anomaly, with the same global coverage and 0.25° grid as the Ssalto/Duacs altimeter products. The estimate is created using a spatial bandpass filter, with cutoff scales of ~1.5° and 10.5°. Zeros in the mask file indicate regions in which it has not been possible to evaluate the quality of the estimate.</p> <p>The cutoff scales applied to the altimetry data were determined through analysis of output from the OceaniC Chaos – ImPacts, strUcture, predicTability (Penduff et al, 2014) experiment, comprising a 50-member ensemble of ocean-sea ice model hindcasts with 0.25° horizontal resolution (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>). The spatiotemporal coherence between the model-based estimates of the atmospherically-forced (ensemble mean) and total simulated sea surface height signals was analysed, and found to exhibit distinct partitioning between the atmospherically-forced and intrinsic contributions in a spatial (but not temporal) sense, thus suggesting that meaningful estimation of the two components can be achieved based on simple spatial filtering. Verification of the method using the model data indicates good accuracy, with a global mean correlation of 0.9 between the estimate based on spatial filtering and the ensemble mean sea surface height. Full details of the methodology and verification may be found in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>.</p> <p>----</p> <p><strong>References</strong>:</p> <p>Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, <a href="https://doi.org/10.5194/gmd-10-1091-2017">doi: 10.5194/gmd-10-1091-2017</a>.</p> <p>Close, S., Penduff, T., Speich, S. and Molines J.-M., 2020. A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations. Progr. Oceanogr. <a href="https://doi.org/10.1016/j.pocean.2020.102314">doi: 10.1016/j.pocean.2020.102314</a></p> <p>Penduff, T., Barnier, B. , Terray, L., Bessières, L., Sérazin, G., Grégorio, S., Brankart, J., Moine, M., Molines, J., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19.</p>
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