# Bering Sea and Western Arctic Coretop Reflectance Database #----------------------------------------------------------------------- # World Data Center for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # NOTE: Please cite original publication, online resource and date accessed when using this data. # If there is no publication information, please cite Investigator, title, online resource and date accessed. # # Description/Documentation lines begin with # # Data lines have no # # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/19560 # Online_Resource: http://www1.ncdc.noaa.gov/pub/data/paleo/paleocean/by_contributor/nwaodua2014/nwaodua2014-loadings.txt # # Archive: Paleoceanography # # Parameter_Keywords: physical properties #--------------------------------------- # Contribution_Date # Date: 2015-12-10 #--------------------------------------- # Title # Study_Name: Bering Sea and Western Arctic Coretop Reflectance Database #--------------------------------------- # Investigators # Investigators: Nwaodua, E.C and Ortiz, J.D. #--------------------------------------- # Description and Notes # Description: These are the reflectance data and varimax rotated principle component analysis from the coretop dataset in publications. Correlation r-value # Magnitudes of the correlations between each component spectrum and the derivative spectrum for a known assemblage: # Correlation r-value # VPCA1 vs. Chlorite+Muscovite 0.856 # VPCA2 vs. Geothite+phycoerythrin+phycocyanin 0.838 # VPCA3 vs. Smectite 0.753 # VPCA4 vs. Calcite+Dolomite 0.909 # VPCA5 vs. Illite+chl_a -0.891 # Provided Keywords: Bering Sea, Western Arctic, Diffuse spectral reflectance , visible derivative spectroscopy, varimax-rotated principal component analysis, sediment provenance #--------------------------------------- # Publication # Authors: Nwaodua, Emmanuel C. and Ortiz, Joseph D. # Published_Date_or_Year: 2014 # Published_Title: Application of quotient normalization technique to minimize errors in diffuse spectral reflectance data collected under various conditions # Journal_Name: J. Sedimentary Res. # Volume: 84 # Issue: # Pages: 729-742 # Report Number: # DOI: 10.2110/jsr.2014.57 # Abstract: Visible and near-infrared (VNIR) derivative spectroscopy of diffuse spectral reflectance (DSR) data can be a potent method to extract lithological information from sediment cores. However, synthesis of multiple DSR data sets collected with different instruments from sediment obtained at different times could subject the DSR measurements to errors arising from disparity in depositional environment, sample processing methods, and storage conditions. Here we apply a quotient normalization technique to set these data in a common reference frame as a first step in our VNIR derivative spectroscopic analysis. The effectiveness of the quotient normalization technique is illustrated with samples from the Bering and west Arctic Sea shelves; this is because of access to sediment samples collected by several coring expeditions at different times in the past over a range of locations. # DSR measurements were obtained from eleven groups of core samples processed under two conditions. Under one of the conditions, they were quotient normalized, while in the other, they were not. Lithological proxies in these cores, from both conditions, were extracted using varimax-rotated, principal-component analysis (VPCA). These lithologies are chlorite+muscovite, goethite+phycoerythrin+phycocyanin, smectite, calcite+dolomite, and illite+chlorophyll a. These lithological proxies were then plotted spatially using GIS kriging software. # The spatial distributions of the VPCA extracted lithologies, under the two conditions, were compared with lithologies obtained by previous workers in the study site. The lithologies after quotient normalization were found to be more consistent with previously published results for clay mineralogy determined by X-ray diffraction (XRD). The quotient normalized data also showed a lower variance in the interpolated data sets relative to the second processing condition, which is the norm, used for comparison. We conclude that the quotient normalization technique is an effective scaling tool for minimizing errors from combining DSR data sets collected during multiple coring expeditions. #--------------------------------------- # Publication # Authors: Nwaodua, Emmanuel C. and Ortiz, Joseph .D., Griffith, Elizabeth M. # Published_Date_or_Year: 2014 # Published_Title: Diffuse spectral reflectance of surficial sediments indicates sedimentary environments on the shelves of the Bering Sea and western Arctic # Journal_Name: Marine Geology # Volume: 355 # Issue: # Pages: 218-233 # Report Number: # DOI: 10.1016/j.margeo.2014.05.023 # Abstract: Visible diffuse spectral reflectance (DSR) has been described by previous workers as an efficient tool for extracting lithological compositions: clay minerals, iron oxyhydroxides, carbonates and diatoms in sedimentary material. The spatial patterns of these components are essential to understanding the processes driving their distribution and accumulation in the marine environment. Some of these processes include sea ice variability, nutrient supply, current flow patterns, current strength, sediment supply, provenance and bathymetry. # The DSR derivatives and Varimax-rotated principal component analysis (VPCA) of the spectral data collected from 234 core samples identified five leading components that account for variations in sediment lithology of the surface samples from the Bering Sea and western Arctic. These spectral components were then matched to spectral standards to identify the mineral assemblages responsible for them. The components responsible for the VPCA trends are chlorite + muscovite; goethite + phycoerythrin + phycocyanin; smectite; calcite + dolomite; and illite + chlorophyll a. # The spatial pattern of the inorganic components is indicative of provenance and processes controlling their transportation and depositional patterns, while the organic components are indicative of areas of primary productivity and driving mechanisms. The chlorite + muscovite assemblage which is sourced dominantly from the Yukon River has maximum values on the Bering Sea shelf; this is the source of the chlorite + muscovite transport to the Chukchi Sea. The chlorite + muscovite assemblage also has a main transport route to the Aleutian basin in the northern Bering Sea shelf. The Chukchi Sea on the western Arctic Ocean shelf is a region of highly reducing condition. This is based on the lowest values for the goethite + phycoerythrin + phycocyanin assemblage, supported by the high carbonate concentrations at this location. The carbonate distribution pattern on the Bering Sea shelf also appears to be tidally influenced. This is because their spatial pattern divides the Bering shelf into three domains, which are similar to the three hydrographic domains on the Bering shelf—a function of tidal influence. The smectite assemblage reaches maximum values along the coast of the Chukotka Peninsula in the Chukchi Sea, denoting the Chukotka Peninsula as a major source area of smectite, and supporting the notion for volcanically derived sediment to the Chukchi Sea. Another likely source of smectite to the Chukchi Sea is via the Bering Strait. The illite in the illite + chlorophyll a assemblage shows maximum values and distribution on the western Arctic Ocean shelf, indicating the western Arctic Ocean shelf to be a main province of illite. All the high values of the illite + chlorophyll a component on the Bering Sea shelf are chlorophyll a from diatoms. # Our results support the usefulness of the DSR method as an effective and efficient tool for provenance analysis, and understanding the processes controlling sediment and biologic distribution, in this case, in the marine environment. #--------------------------------------- # Funding_Agency # Funding_Agency_Name: US National Science Foundation # Grant: ARC-0902818 #--------------------------------------- # Site Information # Site_Name: Bering Sea and Western Arctic # Location: Bering Sea # Country: # Northernmost_Latitude: 80 # Southernmost_Latitude: 50 # Easternmost_Longitude: 130 # Westernmost_Longitude: -135 # Elevation: sea floor #--------------------------------------- # Data_Collection # Collection_Name: Bering Component Loadings Nwaodua14 # First_Year: # Last_Year: 2008 # Time_Unit: AD # Core_Length: # Notes: Undetermined oldest year. Samples are measurements of coretops from multiple sources #--------------------------------------- # Chronology: #--------------------------------------- # Variables # Data variables follow that are preceded by "##" in columns one and two. # Variables list, one per line, shortname-tab-longname components (9 components: what, material, error, units, seasonality, archive, detail, method, C or N for Character or Numeric data) ## Wavelength_nm wavelength,,,nm,,,,,N ## Communality Sum of squares for each wavelength variable over all samples,,,unitless,,,,Varimax-rotated Principal Component Analysis,N ## VPAcomp-Load1 Principal component associated with the eigenvalue for VPCA 1,,,unitless,,Paleoceanography,,Varimax-rotated Principal Component Analysis,N ## Chlor+Musc-ds derivative spectrum used to identify VPCA Component 1,Chlorite+Muscovite ,,percent/nm,,Paleoceanography,,reflectance derivative,N ## VPAcomp-Load2 Principal component associated with the eigenvalue for VPCA 2,,,unitless,,Paleoceanography,,Varimax-rotated Principal Component Analysis,N ## Geoth+phyery+phycoc-ds derivative spectrum used to identify VPCA Component 2,Geothite+phycoerythrin+phycocyanin,,percent/nm,,Paleoceanography,,reflectance derivative,N ## VPAcomp-Load3 Principal component associated with the eigenvalue for VPCA 3,,,unitless,,Paleoceanography,,Varimax-rotated Principal Component Analysis,N ## Smectite-ds derivative spectrum used to identify VPCA Component 3,Smectite,,percent/nm,,Paleoceanography,,reflectance derivative,N ## VPAcomp-Load4 Principal component associated with the eigenvalue for VPCA 4,,,unitless,,Paleoceanography,,Varimax-rotated Principal Component Analysis,N ## Calc+Dolo-ds derivative spectrum used to identify VPCA Component 4,Calcite+Dolomite,,percent/nm,,Paleoceanography,,reflectance derivative,N ## VPAcomp-Load5 Principal component associated with the eigenvalue for VPCA 5,,,unitless,,Paleoceanography,,Varimax-rotated Principal Component Analysis,N ## Illite+chl_a-ds derivative spectrum used to identify VPCA Component 4,Illite+chlorophyll a,,percent/nm,,Paleoceanography,,reflectance derivative,N #------------------------ # Data # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Value: n/a Wavelength_nm Communality VPAcomp-Load1 Chlor+Musc-ds VPAcomp-Load2 Geoth+phyery+phycoc-ds VPAcomp-Load3 Smectite-ds VPAcomp-Load4 Calc+Dolo-ds VPAcomp-Load5 Illite+chl_a-ds 400 0.942 0.267 -0.018 0.187 0.006 -0.192 -0.631 0.893 0.277 -0.034 0.101 410 0.972 0.307 0.016 0.223 0.007 -0.210 -0.662 0.885 0.276 -0.027 0.092 420 0.941 0.411 0.065 0.281 0.007 -0.222 -0.640 0.802 0.243 -0.028 0.082 430 0.932 0.592 0.115 0.293 0.009 -0.180 -0.447 0.680 0.219 -0.033 0.084 440 0.934 0.823 0.143 0.200 0.012 0.002 -0.145 0.465 0.215 0.018 0.096 450 0.941 0.920 0.150 0.079 0.006 0.195 -0.042 0.217 0.161 0.059 0.092 460 0.967 0.892 0.157 0.009 0.003 0.372 0.106 0.151 0.112 0.100 0.072 470 0.944 0.822 0.142 0.022 0.007 0.476 0.271 0.170 0.097 0.108 0.062 480 0.934 0.834 0.115 0.158 0.008 0.430 0.404 0.163 0.069 0.044 0.067 490 0.895 0.803 0.064 0.306 0.012 0.339 0.535 0.200 0.079 0.029 0.080 500 0.866 0.747 0.047 0.386 0.016 0.297 0.521 0.251 0.080 0.090 0.095 510 0.779 0.688 0.040 0.464 0.020 0.225 0.342 0.178 0.084 0.089 0.101 520 0.914 0.715 0.005 0.612 0.025 0.134 0.099 0.076 0.097 0.072 0.096 530 0.914 0.644 -0.009 0.692 0.028 0.051 -0.065 0.122 0.115 0.051 0.085 540 0.837 0.498 -0.015 0.720 0.028 0.065 0.017 0.253 0.072 0.041 0.075 550 0.872 0.413 -0.013 0.799 0.030 0.133 0.206 0.180 0.062 0.115 0.074 560 0.868 0.302 -0.017 0.869 0.032 0.074 0.343 0.055 0.085 0.117 0.072 570 0.951 0.200 -0.038 0.931 0.033 0.125 0.602 0.115 0.090 0.124 0.067 580 0.944 0.083 -0.029 0.910 0.026 0.239 0.836 0.161 0.070 0.164 0.067 590 0.928 0.015 -0.028 0.885 0.018 0.208 0.916 0.264 0.038 0.178 0.060 600 0.878 -0.009 -0.040 0.828 0.014 0.322 0.944 0.189 0.033 0.230 0.046 610 0.884 0.080 -0.080 0.625 0.007 0.660 0.934 0.027 0.021 0.228 0.036 620 0.883 0.197 -0.078 0.387 0.007 0.804 0.922 0.025 0.008 0.218 0.023 630 0.908 0.209 -0.022 0.336 0.010 0.806 0.879 -0.066 0.019 0.311 0.020 640 0.856 0.181 -0.025 0.423 0.010 0.574 0.872 -0.162 0.052 0.537 0.015 650 0.907 0.060 -0.046 0.424 0.007 0.148 0.702 0.005 0.041 0.837 -0.007 660 0.888 0.098 -0.033 0.194 0.005 0.290 0.552 -0.047 0.048 0.869 -0.020 670 0.868 0.255 -0.030 0.123 0.009 0.813 0.695 -0.182 0.050 0.307 0.012 680 0.852 0.315 -0.027 0.108 0.009 0.818 0.766 -0.266 0.011 -0.035 0.081 690 0.890 0.309 -0.008 0.076 0.005 0.843 0.705 -0.280 -0.013 -0.009 0.112 700 0.767 0.214 -0.010 0.079 0.007 0.834 0.411 -0.136 0.021 0.023 0.091