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| Cortese, G; Dolven, Jk; Bjorklund, Kr; Malmgren, Ba. |
| Artificial Neural Networks (ANN) were trained by using an extensive radiolarian census dataset from the Nordic (Greenland, Norwegian, and Iceland) Seas. The regressions between observed and predicted Summer Sea Temperature (SST) indicate that lower error margins and better correlation coefficients are obtained for 100 m (SST100) compared to 10 m (SST10) water depth, and by using a subset of species instead of all species. The trained ANNs were subsequently applied to radiolarian data from two Norwegian Sea cores, HM 79-4 and MD95-2011, for reconstructions of SSTs through the last 15,000 years. The reconstructed SST is quite high during the Bolling-Allerod, when it reaches values only found later during the warmest phase of the Holocene. The climatic... |
| Tipo: Text |
Palavras-chave: Artificial neural networks; Radiolarians; Nordic seas; Late Pleistocene; Holocene. |
| Ano: 2005 |
URL: https://archimer.ifremer.fr/doc/00229/34074/32535.pdf |
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| Weinelt, M; Vogelsang, E; Kucera, M; Pflaumann, U; Sarnthein, M; Voelker, A; Erlenkeuser, H; Malmgren, Ba. |
| Short-term changes in sea surface conditions controlling the thermohaline circulation in the northern North Atlantic are expected to be especially efficient in perturbing global climate stability. Here we assess past variability of sea surface temperature (SST) in the northeast Atlantic and Norwegian Sea during Marine Isotope Stage (MIS) 2 and, in particular, during the Last Glacial Maximum (LGM). Five high-resolution SST records were established on a meridional transect (53 degreesN - 72 degreesN) to trace centennial-scale oscillations in SST and sea-ice cover. We used three independent computational techniques ( SIMMAX modern analogue technique, Artificial Neural Networks ( ANN), and Revised Analog Method ( RAM)) to reconstruct SST from planktonic... |
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| Ano: 2003 |
URL: https://archimer.ifremer.fr/doc/00225/33667/32066.pdf |
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