Sea-surface temperature reconstruction and calibration-function of sediment cores from the Southern Ocean

We provide a new multivariate calibration-function based on South Atlantic modern assemblages of planktonic foraminifera and atlas water column parameters from the Antarctic Circumpolar Current to the Subtropical Gyre and tropical warm waters (i.e., 60°S to 0°S). Therefore, we used a dataset with the abundance pattern of 35 taxonomic groups of planktonic foraminifera in 141 surface sediment samples. Five factors were taken into consideration for the analysis, which account for 93% of the total variance of the original data representing the regional main oceanographic fronts. The new calibration-function F141-35-5 enables the reconstruction of Late Quaternary summer and winter sea-surface temperatures with a statistical error of ~0.5°C. Our function was verified by its application to a sediment core extracted from the western South Atlantic. The downcore reconstruction shows negative anomalies in sea-surface temperatures during the early-mid Holocene and temperatures within the range of modern values during the late Holocene. This pattern is consistent with available reconstructions.

Environmental variables and multivariate seabed classification maps via k means clustering of Potter Cove, Antarctica, link to input and result files in GeoTIFF format

This study subdivides the Potter Cove, King George Island, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis includes in total 42 different environmental variables, interpolated based on samples taken during Australian summer seasons 2010/2011 and 2011/2012. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared and the most reasonable method has been applied. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested and 4, 7, 10 as well as 12 were identified as reasonable numbers for clustering the Potter Cove. Especially the results of 10 and 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.

Secchi depth maps for the Baltic Sea, 2003-2012, with links to GeoTIFs

Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea's optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea's special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model's mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003-2012 come with this paper as Supplementary materials.

(Table 2) Distribution of alkenone unsaturation index UK37 of the eastern North Atlantic

This paper reports the concentrations and within-class distributions of long-chain alkenones and alkyl alkenoates in the surface waters (0-50 m) of the eastern North Atlantic, and correlates their abundance and distribution with those of source organisms and with water temperature and other environmental variables. We collected these samples of >0.8 µm particulate material from the euphotic zone along the JGOFS 20°W longitude transect, from 61°N to 24°N, during seven cruises of the UK-JGOFS Biogeochemical Ocean Flux Study (BOFS) in 1989-1991; the biogeographical range of our 53 samples extends from the cold (<10°C), nutrient-rich and highly productive subarctic waters of the Iceland Basin to the warm (>25°C) oligotrophic subtropical waters off Africa. Surface water concentrations of total alkenone and alkenoates ranged from <50 ng/l in oligotrophic waters below 40°N to 2000-4500 ng/l in high latitude E. huxleyi blooms, and were well correlated with E. huxleyi cell densities, supporting the assumption that E. huxleyi is the predominant source of these compounds in the present day North Atlantic. The within-class distribution of the C37 and C38 alkenones and C36 alkenoates varied strongly as a function of temperature, and was largely unaffected by nutrient concentration, bloom status and other surface water properties. The biosynthetic response of the source organisms to growth temperature differed between the cold (<16°C) waters above 47°N and the warmer waters to the south. In cold (<16°C) waters above 47°N, the relative amounts of alkenoates and C38 alkenones synthesized was a strong function of growth temperature, while the unsaturation ratio of the alkenones (C37 and C38) was uncorrelated with temperature. Conversely, in warm (>16°C) waters below 47°N, the relative proportions of alkenoates and alkenones synthesized remained constant with increasing temperature while the unsaturation ratios of the C37 and C38 methyl alkenones (Uk37 and Uk38Me, respectively) increased linearly. The fitted regressions of Uk37 and Uk38Me versus temperature for waters >16°C were both highly significant (r**2 > 0.96) and had identical slopes (0.057) that were 50% higher than the slope (0.034) of the temperature calibration of Uk37 reported by Prahl and Wakeham (1987; doi:10.1038/330367a0) over the same temperature range. These observations suggest either a physiological adjustment in biochemical response to growth temperature above a 16-17°C threshold and/or variation between different E. huxleyi strains and/or related species inhabiting the cold and warm water regions of the eastern North Atlantic. Using our North Atlantic data set, we have produced multivariate temperature calibrations incorporating all major features of the alkenone and alkenoate data set. Predicted temperatures using multivariate calibrations are largely unbiased, with a standard error of approximately ±1°C over the entire data range. In contrast, simpler calibration models cannot adequately incorporate regional diversity and nonlinear trends with temperature. Our results indicate that calibrations based upon single variables, such as Uk37, can be strongly biased by unknown systematic errors arising from natural variability in the biosynthetic response of the source organisms to growth temperature. Multivariate temperature calibration can be expected to give more precise estimates of Integrated Production Temperatures (IPT) in the sedimentary record over a wider range of paleoenvironmental conditions, when derived using a calibration data set incorporating a similar range of natural variability in biosynthetic response.

Oxygen content and paleoproductivity from surface sediment samples Y69-86 to PLDS-004G

Quantitative estimation of surface ocean productivity and bottom water oxygen concentration with benthic foraminifera was attempted using 70 samples from equatorial and North Pacific surface sediments. These samples come from a well defined depth range in the ocean, between 2200 and 3200 m, so that depth related factors do not interfere with the estimation. Samples were selected so that foraminifera were well preserved in the sediments and temperature and salinity were nearly uniform (T = 1.5° C; S = 34.6 per mil). The sample set was also assembled so as to minimize the correlation often seen between surface ocean productivity and bottom water oxygen values (r**2 = 0.23 for prediction purposes in this case). This procedure reduced the chances of spurious results due to correlations between the environmental variables. The samples encompass a range of productivities from about 25 to >300 gC m**-2 yr**-1, and a bottom water oxygen range from 1.8 to 3.5 ml/L. Benthic foraminiferal assemblages were quantified using the >62 µm fraction of the sediments and 46 taxon categories. MANOVA multivariate regression was used to project the faunal matrix onto the two environmental dimensions using published values for productivity and bottom water oxygen to calibrate this operation. The success of this regression was measured with the multivariate r? which was 0.98 for the productivity dimension and 0.96 for the oxygen dimension. These high coefficients indicate that both environmental variables are strongly imbedded in the faunal data matrix. Analysis of the beta regression coefficients shows that the environmental signals are carried by groups of taxa which are consistent with previous work characterizing benthic foraminiferal responses to productivity and bottom water oxygen. The results of this study suggest that benthic foraminiferal assemblages can be used for quantitative reconstruction of surface ocean productivity and bottom water oxygen concentrations if suitable surface sediment calibration data sets are developed and appropriate means for detecting no-analog samples are found.

Sea surface and the sea floor regionalization of the Southern Ocean by multivariate cluster analysis, links to ArcGIS project files

This study subdivides the Weddell Sea, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis uses 28 environmental variables for the sea surface, 25 variables for the seabed and 9 variables for the analysis between surface and bottom variables. The data were taken during the years 1983-2013. Some data were interpolated. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared for the identification of the most reasonable method. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested. For the seabed 8 and 12 clusters were identified as reasonable numbers for clustering the Weddell Sea. For the sea surface the numbers 8 and 13 and for the top/bottom analysis 8 and 3 were identified, respectively. Additionally, the results of 20 clusters are presented for the three alternatives offering the first small scale environmental regionalization of the Weddell Sea. Especially the results of 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.

A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format

Topographic variation, the spatial variation in elevation and terrain features, underpins a myriad of patterns and processes in geography and ecology and is key to understanding the variation of life on the planet. The characterization of this variation is scale-dependent, i.e. it varies with the distance over which features are assessed and with the spatial grain (grid cell resolution) of analysis. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale basic research and analytical applications, however to date, such technique is unavailable. Here we used the digital elevation model products of global 250 m GMTED and near-global 90 m SRTM to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile and tangential curvature, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches (median, average, minimum, maximum, standard deviation, percent cover, count, majority, Shannon Index, entropy, uniformity). While a global cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains. All newly-developed variables are available for download at http://www.earthenv.org and can serve as a basis for standardized hydrological, environmental and biodiversity modeling at a global extent.