Find research datasets worth reusing

Vertical fluxes of particulate carbon, nitrogen and phosphorus from a sediment trap deployed west of Palmer Station, Antarctica at a depth of 170 meters, 1992-2019.

Particulate organic matter is exported from the upper ocean euphotic zone in the form of large sinking particles and as dissolved material. Particle fluxes to depth link the surface and mesopelagic realm and supply food to the benthos. Sedimentation flux is typically measured with sediment traps of various designs. Palmer LTER has deployed a time-series trap near 64.5degrees S, 66.0degrees W since late 1992. The trap is moored in 300 m depth and collects sinking particles at 150 m. Deployments and analyses were performed by David Karl, University of Hawaii until 2002 when Hugh Ducklow took over the sediment trap operations.Sedimentation at the PAL site of the West Antarctic Peninsula demonstrates extreme seasonality, with a well-defined pulse in the Austral summer following sea ice retreat. Daily sedimentation rates during the summer flux event are among the highest recorded globally. During the Austral winter when the ocean is covered by sea ice and shrouded in darkness, fluxes are among the lowest observed anywhere. Sedimentation rates at PAL typically vary by 4 orders of magnitude. There is also order of magnitude variability in the total annual flux (area under the curve).

Eddy flux measurements during 2015 from low marsh site (Spartina alterniflora) within Shad Creek catchment, Rowley, Massachusetts.

We deployed an eddy covariance system to measure ecosystem-atmosphere exchange of CO2 above a low marsh system (Spartina alterniflora) located within the Shad creek catchment off Plum Island Sound, Rowley MA. The data represents CO2 exchange for all July to October 2015. This site was established in 2015.

Eddy flux measurements during 2016 from low marsh site (Spartina alterniflora) within Shad Creek catchment, Rowley, Massachusetts, PIE LTER.

Eddy flux measurements during 2017 from low marsh site (Spartina alterniflora) within Shad Creek catchment, Rowley, Massachusetts, PIE LTER.

openCC (other)Jun 2025View details →

Advective nitrate fluxes, sea surface chlorophyll concentrations and other physical metrics in the Santa Barbara Channel (2012-2019)

This data package includes 6 files: (1 & 2) In-situ nitrate concentrations at the surface and mixed layer depth, and collocated remotely-sensed and reanalysis quantities of satellite sea surface temperature, 15-day cumulative wind stress, satellite sea surface chlorophyll with a 5-day lag, index of offshore position of the California Current, indices for along-channel and across-channel distance, and index for day of the year. (3) An R script for generating generalized additive models (GAMs) to predict nitrate concentrations at the surface and at the mixed layer depth using the collocated data in files 1 & 2. (4) Daily maps of satellite sea surface chlorophyll concentrations (SSChl), High-frequency radar (HFR) surface currents, weather research and forecasting (WRF) model wind-derived vertical velocities, estimated nitrate concentrations at the surface and mixed layer depth, horizontal advective nitrate fluxes at the surface and vertical advective nitrate fluxes. (5) Daily time series of spatial mean SSChl, principal component amplitude of the first mode of variability in surface currents estimated using complex empirical orthogonal function (EOF) analysis, alongshore pressure gradient, wind stress, spatial mean horizontal velocities at the western and eastern Santa Barbara Channel boundaries, spatial mean vertical velocities, spatial mean surface nitrate concentrations at the channel boundaries and across the entire channel, spatial mean mixed layer depth nitrate concentrations across the entire channel, spatial mean horizontal advective nitrate fluxes at the channel boundaries, and spatial mean vertical advective nitrate fluxes. (6) A MATLAB script for plotting examples of the daily maps and time series in files 4 & 5. These data were processed in order to investigate the impact of local nutrient delivery mechanisms on phytoplankton blooms in the Santa Barbara Channel, California, details of which are available in the study: Brokaw, R.J., D.A. Siegel, L. Washburn,

openCC0Mar 2024View details →

Biome Transition Along Elevational Gradients in New Mexico (SEON) Study: Flux Tower Net Primary Productivity (NPP) Quadrat Study at the Sevilleta National Wildlife Refuge, New Mexico

The varied topography and large elevation gradients that characterize the arid and semi-arid Southwest create a wide range of climatic conditions - and associated biomes - within relatively short distances. This creates an ideal experimental system in which to study the effects of climate on ecosystems. Such studies are critical given that the Southwestern U.S. has already experienced changes in climate that have altered precipitation patterns (Mote et al. 2005), and stands to experience dramatic climate change in the coming decades (Seager et al. 2007; Ting et al. 2007). Climate models currently predict an imminent transition to a warmer, more arid climate in the Southwest (Seager et al. 2007; Ting et al. 2007). Thus, high elevation ecosystems, which currently experience relatively cool and mesic climates, will likely resemble their lower elevation counterparts, which experience a hotter and drier climate. In order to predict regional changes in carbon storage, hydrologic partitioning and water resources in response to these potential shifts, it is critical to understand how both temperature and soil moisture affect processes such as evaportranspiration (ET), total carbon uptake through gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange of carbon, water and energy across elevational gradients. We are using a sequence of six widespread biomes along an elevational gradient in New Mexico -- ranging from hot, arid ecosystems at low elevations to cool, mesic ecosystems at high elevation to test specific hypotheses related to how climatic controls over ecosystem processes change across this gradient. We have an eddy covariance tower and associated meteorological instruments in each biome which we are using to directly measure the exchange of carbon, water and energy between the ecosystem and the atmosphere. This gradient offers us a unique opportunity to test the interactive effects of temperature and soil moisture on ecosystem proce

openCC0Mar 2024View details →

Biome Transition Along Elevational Gradients in New Mexico (SEON) Study: Flux Tower Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico

The varied topography and large elevation gradients that characterize the arid and semi-arid Southwest create a wide range of climatic conditions - and associated biomes - within relatively short distances. This creates an ideal experimental system in which to study the effects of climate on ecosystems. Such studies are critical given that the Southwestern U.S. has already experienced changes in climate that have altered precipitation patterns (Mote et al. 2005), and stands to experience dramatic climate change in the coming decades (Seager et al. 2007; Ting et al. 2007). Climate models currently predict an imminent transition to a warmer, more arid climate in the Southwest (Seager et al. 2007; Ting et al. 2007). Thus, high elevation ecosystems, which currently experience relatively cool and mesic climates, will likely resemble their lower elevation counterparts, which experience a hotter and drier climate. In order to predict regional changes in carbon storage, hydrologic partitioning and water resources in response to these potential shifts, it is critical to understand how both temperature and soil moisture affect processes such as evapotranspiration (ET), total carbon uptake through gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange of carbon, water and energy across elevational gradients. We are using a sequence of six widespread biomes along an elevational gradient in New Mexico -- ranging from hot, arid ecosystems at low elevations to cool, mesic ecosystems at high elevation to test specific hypotheses related to how climatic controls over ecosystem processes change across this gradient. We have an eddy covariance tower and associated meteorological instruments in each biome which we are using to directly measure the exchange of carbon, water and energy between the ecosystem and the atmosphere. This gradient offers us a unique opportunity to test the interactive effects of temperature and soil moisture on ecosystem proces

openCustomJan 2025View details →

VCR Fowling Point Salt Marsh Flux Tower, 2019-2023

This dataset contains eddy covariance CO2 and H2O fluxes, sensible and latent heat fluxes, and meteorological measurements collected in an intertidal salt marsh. The flux tower is part of the Virginia Coastal Reserve Long Term Ecological Research (VCR LTER) site on the Eastern Shore of Virginia, USA. The marsh is on the Atlantic Ocean side of the Delmarva Peninsula and faces shallow coastal lagoons backed by barrier islands. No major rivers drain into the area. The tower is located 2 km from the shoreline and 85 m from a major creek edge. The marsh is dominated by Spartina alterniflora (also referred to as Sporobolus alterniflorus), with an average height of 0.6 m. The semidiurnal tidal cycle inundates the marsh platform twice daily, with 10% of time points having water above the mud platform (indicated by the MHHW dataset variable). Fluxes were measured at 10Hz and averaged over 30 minutes. The CO2 and energy fluxes were gap-filled using the REddyProc software package (doi: 10.5194/bg-15-5015-2018). Using the nighttime approach, the gross primary production and ecosystem respiration fluxes were partitioned from the net ecosystem exchange flux. The research was conducted at the Virginia Coastal Reserve Long Term Ecological Research (VCR LTER) site on the Eastern Shore of Virginia, USA. The flux tower site (37-deg 24'39.85"N, 75-deg 50'0.53"W) a lagoonal salt marsh which is located near the area of Fowling point. The site is located at about 2.2 kilometers away from the mainland and 10.7 kilometers away from Hog Island, the nearest barrier island. The flux tower is situated at about 80 meters away from the creek edge, on the lagoonal salt marsh.

opencc-by-4.0Mar 2020View details →

Carbon and water fluxes in a cork oak woodland in Central Portugal

The Data set contains eddy covariance measurements of carbon and water fluxes and ancillary measurements observed  at a cork oak woodland (Quercus suber L.) in central Portugal. The climate is Mediterranean, with mild, wet winters and hot, dry summers. Data are available in the file data.csv (UTF-8 encoding), the description of the variables and units are available in the file meta.csv (UTF-8 encoding). Further description of the site, methods and data processing can be viewed in the files metadata.pdf and metadata.csv

opencc-by-4.0Mar 2020View details →

A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019)

The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau. The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5) Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange. (6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling (NEE_fsd) as a measure for spatial and temporal variation. References: <ol> <li>Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D., and XU, L. (2008). Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biology, 14, 1854-1876, <a href="https://doi.org/10.1111/j.1365-2486.2008.01606.x">https://doi.org/10.1111/j.1365-2486.2008.01606.x</a>.</li> <li>Finkelstein, P. L. and Sims, P. F. (2001). Sampling error in eddy correlation flux measurements, J. Geophys. Res. Atmos., 106, 3503–3509, doi:10.1029/2000JD900731.</li> <li>Frank, J. M. and Massman, W. J.: A new perspective on the open-path infrared gas analyzer self-heating correction, Agricultural and Forest Meteorology, 290, 107986, doi:10.1016/j.agrformet.2020.107986, 2020.</li> <li>Fratini, G., McDermitt, D. K., and Papale, D. (2004). Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037-1051, <a href="https://doi.org/10.5194/bg-11-1037-2014">https://doi.org/10.5194/bg-11-1037-2014</a>.</li> <li>Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-m., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and valentini, R. (20050. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424-1439, <a href="https://doi.org/10.1111/j.1365-2486.2005.001002.x">https://doi.org/10.1111/j.1365-2486.2005.001002.x</a>.</li> </ol>

opencc-by-4.0Nov 2020View details →

Dataset: Volatile organic compound fluxes in a subarctic peatland and lake

Dataset used in the article "Volatile organic compound fluxes in a subarctic peatland and lake" published in the journal Atmospheric Chemistry and Physics 20: 13399–13416 (2020) <a href="https://doi.org/10.5194/acp-20-13399-2020">https://doi.org/10.5194/acp-20-13399-2020</a> . The tab-delimited file contains direct surface-atmosphere Volatile Organic Compound fluxes, measured by Eddy Covariance with a Proton Transfer Reaction -Time of Flight- Mass Spectrometer (PTR-ToF-MS) at a subarctic fen and a subarctic lake during 2018. It also contains PAR Photosynthetic Active Radiation, air temperature, and vegetation surface temperature.

opencc-by-4.0Dec 2020View details →

TWINS ENA flux and ion temperature data for interval on August 3, 2016

This is a dataset for TWINS ENA flux and calculated ion temperatures used to prepare Figure 1 for a submission to GRL of a paper by A. M. Keesee, N. Buzulukova, C. Mouikis and E. E. Scime  "Mesoscale structures in Earth's magnetotail observed using energetic neutral atom imaging". The dataset has 56 files in .csv format. Files containing the ion temperature (in keV) arrays in the GSM equatorial plane used for Fig. 1a-d have names with format 'temp_YYYYMMDDHHMM.csv' Also included in the dataset are the ENA fluxes projected to the GSM equatorial plane that were used to calculate the ion temperatures. These files have names with formal 'flux[energy]_YYYMMDDHHMM.csv' where [energy] is in keV. The IDL scripts used to create the projections as well as the fitting algorithms used to calculate the ion temperatures are included in Keesee, Amy; Scime, Earl; Zaniewski, Anna; and Katus, Roxanne (2019). 2D Ion Temperature Maps from TWINS ENA data: IDL scripts. UNH Scholars’ Repository <a href="https://dx.doi.org/10.34051/c/2019.1">https://dx.doi.org/10.34051/c/2019.1</a>

opencc-by-4.0Oct 2020View details →

Surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios.

Surface maps and basin mean/total of annual mean surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios and for underlying the baseline projection (RCP4.5). Details on simulations and alkalinisation strategies are given in the reference article below.   Reference: Butenschön, M., Lovato, T., Masina, S., Caserini, S., Grosso, M., 2021. Alkalinization Scenarios in the Mediterranean Sea for Efficient Removal of Atmospheric CO2 and the Mitigation of Ocean Acidification. Front. Clim. 3. <a href="https://doi.org/10.3389/fclim.2021.614537">https://doi.org/10.3389/fclim.2021.614537</a>

opencc-by-4.0Nov 2024View details →

RADIT: A Machine Learning-Reconstructed Dataset of River Discharge, Temperature, and Heat Flux into the Arctic Ocean

The Reconstructed Arctic-draining river DIscharge and Temperature (RADIT) dataset provides daily records of river discharge, temperature, and heat flux for 25 major Arctic-draining rivers from 1950 to 2023. Using machine learning methods and ERA5-Land reanalysis data, we reconstructed these key hydrological variables with high accuracy (most NSEs > 0.8). Due to licensing restrictions and to encourage adherence to the stated licenses of the original input data, this dataset only provides the reconstructed (filled) values. Users can obtain the complete historical observational data from their original publicly available sources as detailed in our documentation. By combining these original observations with our reconstructed data, a comprehensive and continuous daily dataset from 1950 to 2023 can be assembled. Clear instructions and links for downloading the original observational data used in this study can be found at: <a href="https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data" target="_blank" rel="noopener">https://github.com/zhwang24/RADIT-Reconstructed-Arctic-River-Data</a>. Should you encounter any issues or have questions, please feel free to contact the first author, Zihan Wang (zhwang2018@163.com).

opencc-by-4.0Nov 2024View details →

Proton fluxes from the REleASE system from 1995 to 2016

This dataset has been generated utilizing the REleASE method described in Posner (2007, doi:10.1029/2006SW000268). It utilizes data from the Electron Proton Helium INstrument aboard SOHO (Müller-Mellin et al., 1995, doi:10.1007/BF00733437).  Yearly files of 22 proton energy bins created from level 1 EPHIN .PHA and .SCI data from 1995 to 2016. Each file contains data with a 1 minute time resolution.    Contents of each file: Columns  #1:     year #2:     day of year (DOY) #3:     milliseconds of the day #4:     flag for data status: ->      0 = not nominal data (do not use) ->      1 = ok #5:     criterium for high fluxes: ->      1 if count rate in anti coincidence > 25e5 * 59.95312 ->      1 if a00+a01+a02+a03+a04+a05 count rate > 47500 * 59.95312 ->      else 0 #6:     <2: ring on, >=2: ring off proton fluxes in (cm^2 s sr MeV)^-1 in the folowing energy bins #7:     3.98 - 4.47 MeV #8:     4.47 - 5.01 MeV #9:     5.01 - 5.62 MeV #10:    5.62 - 6.31 MeV #11:    6.31 - 7.08 MeV #12:    7.08 - 7.94 MeV #13:    7.94 - 8.91 MeV #14:    8.91 - 10.00 MeV #15:    10.00 - 11.22 MeV #16:    11.22 - 12.59 Mev #17:    12.59 - 14.13 MeV #18:    14.13 - 15.85 MeV #19:    15.85 - 17.78 MeV #20:    17.78 - 19.95 MeV #21:    19.95 - 22.39 MeV #22:    22.39 - 25.12 MeV #23:    25.12 - 28.18 MeV #24:    28.18 - 31.62 MeV #25:    31.62 - 35.48 MeV #26:    35.48 - 39.81 MeV #27:    39.81 - 44.67 MeV #28:    44.67 - 50.12 MeV

opencc-by-4.0Oct 2024View details →

FluxDataKit v3.4.2: A comprehensive data set of ecosystem fluxes for land surface modelling

The Flux data kit is an effort to expand upon the existing work by Ukkola et a. (2022) to synthesize various sources of ecosystem flux data (i.e. the PLUMBER2 data set, gathered from all major networks). We further expand upon the original data set by integrating data which was either expanded upon (temporally) or where sites were added (e.g. the integration of ICOS data). The effort uses the FluxnetLSM package by the above mentioned authors, as well as their general workflow. In contrast to the PLUMBER2 data set we do not apply stringent quality control, and all quality control on the availability of variables and/or their duration should be done by the user. Furthermore, we include both leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the netcdf output, where PLUMBER2 only provided LAI. On all other parts the formatting and naming conventions as well as quality control specifications remain the same as in PLUMBER2. We therefore refer to Ukkola et al. (2022) for details. Data included The data included consists of the following files, containing different versions of the same data and site meta information. <ul> <li><code>FLUXDATAKIT_LSM.tar.gz</code> file contains compressed NetCDF files compatible with the ALMA scheme for land surface modelling. </li> <li><code>FLUXDATAKIT_FLUXNET.tar.gz</code> file contains data in a CSV format according to the FLUXNET specifications.</li> <li><code>rsofun_driver_data_v3.3.rds</code> file is a compressed serialized R file containing data formatted for use with the {rsofun} R package.</li> <li><code><a href="../api/records/11370417/draft/files/fdk_site_info.csv/content" target="_blank" rel="noopener noreferrer">fdk_site_info.csv</a></code> contains site meta information in tabular form</li> <li><a href="../api/records/11370417/draft/files/fdk_site_fullyearsequence.csv/content" target="_blank" rel="noopener noreferrer"><code>fdk_site_fullyearsequence.csv</code></a> contains information about complete sequences of good-quality data by site (see also <a href="https://geco-bern.github.io/FluxDataKit/articles/04_data_use.html">here</a>).</li> </ul> Data generation Data is generated using the FluxDataKit project. Although this project is not meant for continuous releases, and no support is provided in using this code with data provided AS IS, it might still be useful to some: <a href="https://github.com/geco-bern/FluxDataKit">https://github.com/geco-bern/FluxDataKit</a> The data can be further complimented using the FluxnetEO dataset, which is accessible through the package with the same name as found here: <a href="https://github.com/geco-bern/FluxnetEO">https://github.com/geco-bern/FluxnetEO</a> Acknowledgements The flux data kit is part of the LEMONTREE project and funded by Schmidt Futures and under the umbrella of the Virtual Earth System Research Institute (VESRI). References: <ul> <li>Ukkola, Anna M., Gab Abramowitz, and Martin G. De Kauwe. "A flux tower dataset tailored for land model evaluation." Earth System Science Data 14.2 (2022): 449-461.</li> </ul>

opencc-by-4.0Dec 2021View details →

Martian crater ages and crater counting - Does the impact flux of small and large asteroids varied through time on Mars, the Earth and the Moon?

<ul> <li>The SM_mars_crater_dating.xlsx table contains all the information used to date the 49 martian impact craters considered in this study (< 600 Ma). </li> </ul> <ol> <li>CRATER ID </li> <li>CRATER NAME</li> <li>DIAM KM </li> <li>LAT </li> <li>LONG </li> <li>DEPTH RIM KM </li> <li>DEPTH SURF KM </li> <li>DEPTH FLOOR KM </li> <li>NUMBER LAYER</li> <li>MORPHO EJECTA </li> <li>PRESERVATION </li> <li>COUNT AREA KM2: counting area from ejecta banket mapping </li> <li>COUNT AREA ASCI* KM2: counting area after removal of surfaces contaminated by secondary craters </li> <li>THRESHOLD AREA KM2: minimum size of Voronoi polygon area below which all associated detected craters are considered of secondary origin</li> <li>NB SEC: number of secondary craters dentified by ASCI </li> <li>PERCENT SEC</li> <li>NB CRAT 100M: total number of craters > 100 m detected by the CDA** on the CTX global mosaic*** over the counting area</li> <li>NB PRIM 100M: number of craters identified as primaries by ASCI</li> <li>TURNOFF DIAM KM: minimum crater diameter used to fit the crater-size frequency distribution (CSFD) with an isochron</li> <li>NB CRAT FIT: number of craters used to fit the CSFD with an isochron</li> <li>AGE GA: model age based on Hartmann (2005) chronology model**** and Michael et al. (2016) fitting technique*****</li> <li>AGE MAX GA</li> <li>AGE MIN GA</li> <li>N(1): equivalent number of accumulated craters >1km per km2</li> <li>N(1) MAX</li> <li>N(1) MIN</li> </ol> *ASCI: Automatic Secondary Crater Identification: A. Lagain, K. Servis, G. K. Benedix, C. Norman, S. Anderson, P. A. Bland, Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods, Earth and Space Science 8 (2) (2021). doi:10.1029/2020EA001598. **CDA: Crater Detection Algorithm: G. K. Benedix, A. Lagain, K. Chai, S. Meka, S. Anderson, C. Norman, P. A. Bland, J. Paxman, M. C. Towner, T. Tan, Deriving Surface Ages on Mars Using Automated Crater Counting, Earth and Space Science 7 (3) (2020). doi:10.1029/2019EA001005. *** CTX global mosaic: Context Camera global mosaic: J. L. Dickson, L. A. Kerber, C. I. Fassett, B. L. Ehlmann, A Global, Blended CTX Mosaic of Mars with Vectorized Seam Mapping: A New Mosaicking Pipeline Using Principles of Non-Destructive Image Editing, in: Lunar and Planetary Science Conference (2018), p. 2480. **** W. K. Hartmann, Martian cratering 8: Isochron refinement and the chronology of Mars, Icarus 174 (2) (2005) 294–320. doi:10.1016/j.icarus.2004.11.023. ***** G. G. Michael, T. Kneissl, A. Neesemann, Planetary surface dating from crater size-frequency distribution measurements: Poisson timing analysis, Icarus 277 (2016) 279–285. doi:10.1016/j.icarus.2016.05.019. <ul> <li>The crater_counting.csv table contains the location and size of impact craters used to derive the ages of the 49 craters younger than 600 Ma old presented in this study. </li> </ul>

opencc-by-4.0Jan 2022View details →

TOMCAT CTM simulated ozone profiles using NRL2, SATIRE and SORCE solar fluxes

Individual file contain TOMCAT CTM simulated ozone profiles from five model simulations analysed in the following publication. Briefly,  vmro3_T2Mz_TOMCAT_A_NRL2_2005-2020.nc contain ozone profiles from the control simulation that uses ERA5 dynamical forcing fields and NRL V2 solar fluxes vmro3_T2Mz_TOMCAT_B_SATIRE_2005-2020.nc  and vmro3_T2Mz_TOMCAT_C_SORCE_2005-2020.nc contain ozone profiles from a simulations that are  similar to the control simulation but with SATIRE and SORCE solar fluxes vmro3_T2Mz_TOMCAT_D_SFix_2005-2020.nc has ozone profiles from simulation that is similar to the control simulation but with fixed solar fluxes, whereas vmro3_T2Mz_TOMCAT_E_DFix_2005-2020.nc also contain ozone profiles from a simulation where model uses annually repeating dynamical fields.   Dhomse, S. S., Chipperfield, M. P., Feng, W., Hossaini, R., Mann, G. W., Santee, M. L., and Weber, M.: A Single-Peak-Structured Solar Cycle Signal in Stratospheric Ozone based on Microwave Limb Sounder Observations and Model Simulations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-663, in review, 2021.

opencc-by-4.0May 2022View details →

Seafloor organic carbon flux output from the NEMO-MEDUSA model

This output was produced by a simulation using a coupled ocean physics and marine biogeochemistry model. The physical ocean submodel was the Nucleus for European Modeling of the Ocean (NEMO) physical ocean model (Madec, 2014), run here in a global 1/12-degree resolution configuration (ORCA0083). The marine biogeochemistry submodel was the Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification (MEDUSA-2), an intermediate-complexity plankton ecosystem model (Yool et al., 2013). The horizontal resolution of this configuration of NEMO has non-uniform grid cells ranging 2 to 9 km in size (mean 7.5 km), with 75 vertical depth levels (31 levels between the surface and 200 m depth). Sea-ice is represented in the model by the Louvian‐la‐Neuve Ice Model (LIM2) (Fichefet, & Maqueda, M. a. M., 1997; Goosse & Fichefet, 1999). The configuration was forced at the air-sea interface with version 5.2 of the DRAKKAR forcing set (DFS) (Brodeau et al., 2010). DFS 5.2 is based on ERA40 reanalysis data, comprising of 6‐hourly means for wind, humidity, and atmospheric temperature, daily means for radiative fluxes (both longwave and shortwave), and monthly means for precipitation. A monthly climatology was used for river runoff, taken from the CORE2 reanalysis (Brodeau et al., 2010; Timmermann et al., 2005). The resulting model hindcast was created using this forcing set for the period 1958–2015, with marine biogeochemistry initialised in 1990. This archive includes the flux of organic carbon reaching the seafloor and the area of the grid cells for the global domain. In MEDUSA, the seafloor flux is the sum of slow- and fast-sinking detrital particles that reach the base of the water column and enter the benthic submodel of MEDUSA. In general, away from shallow water regions (< 200 m), this flux is dominated by fast-sinking material produced by ecological processes associated with the large components of MEDUSA. The specific subset of output used was drawn from the decadal period 2006-2015, and was regridded from the non-uniform ORCA0083 grid to a regular 1/12-degree grid. Output processing was undertaken by A. Yool (axy@noc.ac.uk; National Oceanography Centre, Southampton UK). In addition to the netCDF files, text file dumps of their contents are included to assist with interpretation. References: Brodeau, L., Barnier, B., Treguier, A.‐M., Penduff, T., & Gulev, S. (2010). An ERA40‐based atmospheric forcing for global ocean circulation models. Ocean Modelling, 31, 88–104. Fichefet, T., & Maqueda, M. a. M. (1997). Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. Journal of Geophysical Research, Oceans, 102, 12,609–12,646. Goosse, H., & Fichefet, T. (1999). Importance of ice‐ocean interactions for the global ocean circulation: A model study. Journal of Geophysical Research, Oceans, 104, 23,337–23,355. Kelly, S., Popova, E., Aksenov, Y., Marsh, R., & Yool, A. (2018). Lagrangian modeling of Arctic Ocean circulation pathways: Impact of advection on spread of pollutants. J. Geophys. Res. Oceans, 123, 2882‐2902, doi: 10.1002/2017JC013460. Madec, G. (2014). "NEMO Ocean engine" (draft edition r5171) "NEMO Ocean engine" (draft edition r5171). Note du Pôle de modélisation, Institut Pierre‐Simon Laplace (IPSL), France, 27, 1288–1619. Timmermann, R., Goosse, H., Madec, G., Fichefet, T., Ethe, C., & Dulière, V. (2005). On the representation of high latitude processes in the ORCA‐LIM global coupled sea ice–ocean model. Ocean Modelling, 8, 175–201. Yool, A., Popova, E.E. and Anderson, T.R. (2013).  MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies.  Geoscientific Model Development 6, 1767-1811, doi: 10.5194/gmd-6-1767-2013.