132 resultados para spatial and temporal variability


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We present for the first time all 12 d18O records obtained from ice cores drilled in the framework of the North Greenland Traverse (NGT) between 1993 and 1995 in northern Greenland. The cores cover an area of 680 km × 317 km, 10 % of the Greenland ice sheet. Depending on core length (100-175 m) and accumulation rate (90-200 kg/m**2/a) the single records reflect an isotope-temperature history over the last 500-1100 years. Lowest d18O mean values occur north of the summit and east of the main divide as a consequence of Greenland's topography. In general, ice cores drilled on the main ice divide show different results than those drilled east of the main ice divide that might be influenced by secondary regional moisture sources. A stack of all NGT records and the NGRIP record is presented with improved signal-to-noise ratio. Compared to single records, this stack represents the mean d18O signal for northern Greenland that is interpreted as proxy for temperature. Our northern Greenland d18O stack indicates distinctly enriched d18O values during medieval times, about AD 1420 ± 20 and from AD 1870 onwards. The period between AD 1420 and AD 1850 has depleted d18O values compared to the average for the entire millennium and represents the Little Ice Age. The d18O values of the 20th century are comparable to the medieval period but are lower than that about AD 1420.

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Planktic stable isotopes by Mix for this paper and Pisias and Mix (1997)

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We have developed a statistical gap-filling method adapted to the specific coverage and properties of observed fugacity of surface ocean CO2 (fCO2). We have used this method to interpolate the Surface Ocean CO2 Atlas (SOCAT) v2 database on a 2.5°×2.5° global grid (south of 70°N) for 1985-2011 at monthly resolution. The method combines a spatial interpolation based on a 'radius of influence' to determine nearby similar fCO2 values with temporal harmonic and cubic spline curve-fitting, and also fits long term trends and seasonal cycles. Interannual variability is established using deviations of observations from the fitted trends and seasonal cycles. An uncertainty is computed for all interpolated values based on the spatial and temporal range of the interpolation. Tests of the method using model data show that it performs as well as or better than previous regional interpolation methods, but in addition it provides a near-global and interannual coverage.

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Studies of spatial and temporal changes in modern and past sea-ice occurrence may help to understand the processes controlling the recent decrease in Arctic sea-ice cover. Here, we determined concentrations of IP25, a novel biomarker proxy for sea ice developed in recent years, phytoplankton-derived biomarkers (brassicasterol and dinosterol) and terrigenous biomarkers (campesterol and ß-sitosterol) in the surface sediments from the Kara and Laptev seas to estimate modern spatial (seasonal) sea-ice variability and organic-matter sources. C25-HBI dienes and trienes were determined as additional paleoenvironmental proxies in the study area. Furthermore, a combined phytoplankton-IP25 biomarker approach (PIP25 index) is used to reconstruct the modern sea-ice distribution more quantitatively. The terrigenous biomarkers reach maximum concentrations in the coastal zones and estuaries, reflecting the huge discharge by the major rivers Ob, Yenisei and Lena. Maxima in phytoplankton biomarkers indicating increased primary productivity were found in the seasonally ice-free central part of the Kara and Laptev seas. Neither IP25 nor PIP25, however, show a clear and simple correlation with satellite sea-ice distribution in our study area due to the complex environmental conditions in our study area and the transportation process of sea-ice diatom in the water column. Differences in the diene/IP25 and triene/IP25 ratios point to different sources of these HBIs and different environmental conditions. The diene/IP25 ratio seems to correlate positively with sea-surface temperature, while negatively with salinity distributions.