An official website of the United States government
We present high resolution measurements of atmospheric methane (CH4) and nitrogen isotopic composition (d15N-N2) in the Greenland Ice Sheet Project Two (GISP2) Ice core. The data span Marine Isotope Stage 3, 13 to 50 thousand years (ka) before present. These datasets enhance our understanding of abrupt climate variability during the last glacial period, with a focus on Heinrich events 1 through 5. CH4 data were analyzed between 2014 and 2020 via an established wet extraction technique (Mitchell et al. 2013). Concentrations were determined via gas chromatography measurements on an Agilent 6890N and calibrated to the NOAA04 scale. d15N-N2 data were measured between 2017 and 2020 on a Finnigan MAT Delta XP via an established technique (Petrenko et al. 2006). The methane data allow for gas-phase synchronization of the GISP2 ice core to other polar ice cores from Greenland and Antarctica. The nitrogen isotopic composition data allow for reconstruction of abrupt Greenland surface climate variations.
more »
« less
CO2 amount fractions from WAIS Divide, Antarctica
This data set contains measurements of carbon dioxide (CO2) amount fractions in gas bubbles from the WAIS Divide ice core WD06. All measurements were made in the Ice Core Laboratory at Oregon State University in Corvallis, Oregon USA. The data set includes the replicate-mean values and measurement precision (1 sigma standard error) from all CO2 measurements published in Wendt et al. (2024) PNAS. Bauska et al. (2021) Nature Geoscience, and Marcott et al. (2014) Nature. See respective publications for details. Ages listed in years before 1950 AD on the WD2014 timescale (see Buizert et al., 2015 and Sigl et al., 2014 for chronology details).
more »
« less
- Award ID(s):
- 1906143
- PAR ID:
- 10559625
- Publisher / Repository:
- U.S. Antarctic Program (USAP) Data Center
- Date Published:
- Subject(s) / Keyword(s):
- CO2 Ice Core Data Cryosphere WAIS Divide Ice Core
- Format(s):
- Medium: X
- Location:
- Antarctica; West Antarctic Ice Sheet Divide; (Latitude:-79; Longitude:-112)
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
This data set contains replicate measurements of D17O from a 92-m deep section of core at Summit, Greenland and other operational data necessary to reconstruct figures presented in Davidge et al., 2022. Details of the entire dataset and a description of the relevant methods can be found in Davidge et al., 2022. The names of each sheet indicate the corresponding figure numbers. The D17O record spans about two years of accumulation. Discrete measurements by laser spectroscopy are provided at 1.4-cm resolution. For the purpose of method development, continuous-flow measurements are provided with nine replicates and are averaged to multiple depth resolutions as described in Davidge et al., 2022.more » « less
-
Hyperspectral imaging (HSI) technology has been increasingly used in Earth and planetary sciences. This imaging technique has been successfully tested on ice cores using VNIR (visible and near-infrared, 380-1000 nm) (Garzonio et al., 2018) and near-infrared (900 - 1700 nm) (McDowell et al, 2023) line-scan cameras. Results show that HSI data greatly expand ice core line-scan imaging capabilities, previously used with gray or RGB cameras (see summary in Dey et al., 2023). Combinations of selected HSI bands from the hyperspectral data cube improve feature detection in ice core stratigraphy, and map distribution of volcanic material, dust, air bubbles, fractures, and ice crystals in ice cores. Captured spectral information provides unique fingerprints for specific materials present in ice cores. This method helps to guide ice core sampling because it provides non-destructive, rapid visualization of microstructural properties, layering, bubble contents, increases in dust, or presence of tephra material. Precise identification of these atmospheric components is important for understanding past climate drivers reconstructed from ice cores. As part of the COLDEX project (Brook et al., this meeting) we adapted the SPECIM SisuSCS HSI system for ice core imaging. The ice core scanning system is housed inside the ca. -20ºC main NSF ICF freezer, and externally computer-controlled. The operator monitors scanning operations and communicates with personnel inside of the freezer via radio. The system is equipped with a SPECIM FX10 camera that measures up to 224 bands in the VNIR range. We modified the ice core holder tray and installed a heated enclosure for the camera. The system uses SCHOTT DCR III Fiber Optic light sources with an OSL2BIR bulb from Thorlabs. IR filters are removed to extend the light spectral range beyond the 700 nm limit without heating the ice core surface during rapid (<5 minutes) scanning of an entire meter-long section. Emitted light enters ice at a 45º angle from two top and two bottom light sources. To calibrate absolute reflectance we use three Spectralon panels with 100, 50 and 20% reflectance values with every scan as well as several secondary reflective standards and USAF targets for geometric corrections. We are developing Python-based open source data processing routines and currently comparing HSI data with existing ice core physical and chemical measurements. The goal is to fully integrate the ice core HSI system with ice core processing at the NSF ICF. Dey et al., 2023. Application of Visual Stratigraphy from Line-Scan Images to Constrain Chronology and Melt Features of a Firn Core from Coastal Antarctica. Journal of Glaciology 69(273): 179–90. https://doi.org/10.1017/jog.2022.59.Garzonio et al., 2018. A Novel Hyperspectral System for High Resolution Imaging of Ice Cores: Application to Light-Absorbing Impurities and Ice Structure. Cold Regions Science and Technology 155: 47–57. https://doi.org/10.1016/j.coldregions.2018.07.005.McDowell et al., 2023. A Cold Laboratory Hyperspectral Imaging System to Map Grain Size and Ice Layer Distributions in Firn Cores. Preprint. Ice sheets/Instrumentation. https://doi.org/10.5194/egusphere-2023-2351.more » « less
-
The phenology of critical biological events in aquatic ecosystems are rapidly shifting due to climate change. Growing variability in phenological cues can increase the likelihood of trophic mismatches, causing recruitment failures in commercially, culturally, and recreationally important fisheries. We tested for changes in spawning phenology of regionally important walleye (Sander vitreus) populations in 194 Midwest US lakes in Minnesota, Michigan, and Wisconsin spanning 1939-2019 to investigate factors influencing walleye phenological responses to climate change and associated climate variability, including ice-off timing, lake physical characteristics, and population stocking history. Data from Wisconsin and Michigan lakes (185 and 5 out of 194 total lakes, respectively) were collected by the Wisconsin Department of Natural Resources (WDNR) and the Great Lakes Indian Fish and Wildlife Commission (GLIFWC) through standardized spring walleye mark-recapture surveys and spring tribal harvest season records. Standardized spring mark-recapture population estimates are performed shortly after ice-off, where following a marking event, a subsequent recapture sampling event is conducted using nighttime electrofishing (typically AC – WDNR, pulsed-DC – GLIFWC) of the entire shoreline including islands for small lakes and index stations for large lakes (Hansen et al. 2015) that is timed to coincide with peak walleye spawning activity (G. Hatzenbeler, WDNR, personal communication; M. Luehring, GLIFWC, personal communication; Beard et al. 1997). Data for four additional Minnesota lakes were collected by the Minnesota Department of Natural Resources (MNDNR) beginning in 1939 during annual collections of walleye eggs and broodstock (Schneider et al. 2010), where date of peak egg take was used to index peak spawning activity. For lakes where spawning location did not match the lake for which the ice-off data was collected, the spawning location either flowed into (Pike River) or was within 50 km of a lake where ice-off data were available (Pine River) and these ice-off data were used. Following the affirmation of off-reservation Ojibwe tribal fishing rights in the Ceded Territories of Wisconsin and the Upper Peninsula of Michigan in 1987, tribal spearfishers have targeted walleye during spring spawning (Mrnak et al. 2018). Nightly harvests are recorded as part of a compulsory creel survey (US Department of the Interior 1991). Using these records, we calculated the date of peak spawning activity in a given lake-year as the day of maximum tribal harvest. Although we were unable to account for varying effort in these data, a preliminary analysis comparing spawning dates estimated using tribal harvest to those determined from standardized agency surveys in the same lake and year showed that they were highly correlated (Pearson’s correlation: r = 0.91, P < 0.001). For lakes that had walleye spawning data from both agency surveys and tribal harvest, we used the data source with the greatest number of observation years. Ice-off phenology data was collected from two sources – either observed from the Global Lake and River Ice Phenology database (Benson et al. 2000)t, or modeled from a USGS region-wide machine-learning model which used North American Land Data Assimilation System (NLDAS) meteorological inputs combined with lake characteristics (lake position, clarity, size, depth, hypsography, etc.) to predict daily water column temperatures from 1979 - 2022, from which ice-off dates could be derived (https://www.sciencebase.gov/catalog/item/6206d3c2d34ec05caca53071; see Corson-Dosch et al. 2023 for details). Modeled data for our study lakes (see (Read et al. 2021) for modeling details), which performed well in reflecting ice phenology when compared to observed data (i.e., highly significant correlation between observed and modeled ice-off dates when both were available; r = 0.71, p < 0.001). Lake surface area (ha), latitude, and maximum depth (m) were acquired from agency databases and lake reports. Lake class was based on a WDNR lakes classification system (Rypel et al. 2019) that categorized lakes based on temperature, water clarity, depth, and fish community. Walleye stocking history was defined using the walleye stocking classification system developed by the Wisconsin Technical Working Group (see also Sass et al. 2021), which categorized lakes based on relative contributions of naturally-produced and stocked fish to adult recruitment by relying heavily on historic records of age-0 and age-1 catch rates and stocking histories. Wisconsin lakes were divided into three groups: natural recruitment (NR), a combination of stocking and natural recruitment (C-ST), and stocked only (ST). Walleye natural recruitment was indexed as age-0 walleye CPE (number of age-0 walleye captured per km of shoreline electrofished) from WDNR and GLIFWC fall electrofishing surveys (see Hansen et al. 2015 for details). We excluded lake-years where stocking of age-0 fish occurred before age-0 surveys to only include measurements of naturally-reproduced fish.more » « less
-
Refractory black carbon (rBC) aerosols in air and precipitation result from incomplete combustion. Prior to 18th century industrialization, the primary emission sources were wildfires and agricultural fires. After industrialization, fossil fuel burning has become an important emission source as well. Because these aerosols are import contributors to Earth’s radiative forcing both in the air and when deposited to bright surfaces such as fresh snow, quantifying past rBC emissions is critical to accurate Earth System Modeling. This data set contains 1750 to 2010 annual rBC depositional fluxes measured in a global array of 31, mostly polar ice cores. They were used (Zhang et al., Nature Communications, 2024) to reconstruct atmospheric rBC emissions using the atmospheric chemical transport GEOS-Chem. Details on the rBC measurement methods and chronology development are provided in the associated references for the individual ice core records.more » « less
