A high‐resolution radiolarian‐derived paleotemperature record for the Late Pleistocene‐Holocene in the Norwegian Sea

Dolven, Jane K.; Cortese, Giuseppe; Bjørklund, Kjell R.

doi:10.1029/2002pa000780

Cited by 54 publications

(39 citation statements)

References 41 publications

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“…Modern SST values for 10 m water depth during summer (July, August, September) for the calibration dataset were taken from the World Ocean Atlas version 2 (WOA, 1998). In addition to the foraminifer-based record, SST estimates for MD95-2011 are available from alkenones Jansen et al, 2008), diatoms (Birks et al, 2002;Andersen et al, 2004), and radiolarians (Dolven et al, 2002). Here, we compare results from MD95-2011 with newly published foraminifer SST records from the northern North Atlantic.…”

Section: Methodsmentioning

confidence: 95%

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Holocene trends in the foraminifer record from the Norwegian Sea and the North Atlantic Ocean

et al. 2010

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Abstract. The early to mid-Holocene thermal optimum is a well-known feature in a wide variety of paleoclimate archives from the Northern Hemisphere. Reconstructed summer temperature anomalies from across northern Europe show a clear maximum around 6000 years before present (6 ka). For the marine realm, Holocene trends in sea-surface temperature reconstructions for the North Atlantic and Norwegian Sea do not exhibit a consistent pattern of early to midHolocene warmth. Sea-surface temperature records based on alkenones and diatoms generally show the existence of a warm early to mid-Holocene optimum. In contrast, several foraminifer and radiolarian based temperature records from the North Atlantic and Norwegian Sea show a cool midHolocene anomaly and a trend towards warmer temperatures in the late Holocene. In this paper, we revisit the foraminifer record from the Vøring Plateau in the Norwegian Sea. We also compare this record with published foraminifer based temperature reconstructions from the North Atlantic and with modelled (CCSM3) upper ocean temperatures. Model results indicate that while the seasonal summer warming of the seasurface was stronger during the mid-Holocene, sub-surface depths experienced a cooling. This hydrographic setting can explain the discrepancies between the Holocene trends exhibited by phytoplankton and zooplankton based temperature proxy records.

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Section: Methodsmentioning

confidence: 95%

“…The zooplankton-based records do not display an early to mid-Holocene warmth and declining temperatures towards the late Holocene. The transfer function based summer SST reconstruction from radiolarians (Dolven et al, 2002) (Fig. 6e) and foraminifers (Fig.…”

Section: Holocene Ssts In the Norwegian Seamentioning

confidence: 99%

Holocene trends in the foraminifer record from the Norwegian Sea and the North Atlantic Ocean

et al. 2010

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“…between 8.5 and 5.5 ka BP) at the Vøring Plateau. Moreover, at the same site, SST reconstructions based on foraminifera and radiolarians also lack this early HTM suggested by diatoms (Risebrobakken et al, 2003(Risebrobakken et al, , 2011Cortese et al, 2005;Dolven et al, 2002). Jansen et al (2008) suggest that the SST maximum recorded in proxy data above the seasonal thermocline (diatoms, alkenones) is forced by the summer insolation maximum and that deeper dwelling species (foraminifera and radiolarians) are not influenced.…”

Section: Introductionmentioning

confidence: 99%

The Holocene thermal maximum in the Nordic Seas: the impact of Greenland Ice Sheet melt and other forcings in a coupled atmosphere–sea-ice–ocean model

Blaschek¹,

Renssen²

2013

Clim. Past

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Abstract. The relatively warm early Holocene climate in the Nordic Seas, known as the Holocene thermal maximum (HTM), is often associated with an orbitally forced summer insolation maximum at 10 ka BP. The spatial and temporal response recorded in proxy data in the North Atlantic and the Nordic Seas reveals a complex interaction of mechanisms active in the HTM. Previous studies have investigated the impact of the Laurentide Ice Sheet (LIS), as a remnant from the previous glacial period, altering climate conditions with a continuous supply of melt water to the Labrador Sea and adjacent seas and with a downwind cooling effect from the remnant LIS. In our present work we extend this approach by investigating the impact of the Greenland Ice Sheet (GIS) on the early Holocene climate and the HTM. Reconstructions suggest melt rates of 13 mSv for 9 ka BP, which result in our model in an ocean surface cooling of up to 2 K near Greenland. Reconstructed summer SST gradients agree best with our simulation including GIS melt, confirming that the impact of the early Holocene GIS is crucial for understanding the HTM characteristics in the Nordic Seas area. This implies that modern and near-future GIS melt can be expected to play an active role in the climate system in the centuries to come.

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“…IKTF has been applied to radiolarians from the Pacific Ocean (Moore, 1973;Molina-Cruz, 1984;Pisias et al, 1997), the Southern Ocean (Abelmann et al, 1999;Cortese and Abelmann, 2002), the Atlantic Ocean (Morley, 1979), and the Nordic Seas (Bjørklund et al, 1998;Dolven et al, 2002;Cortese et al, 2003). In this paper, ANNs were trained on the basis of radiolarian relative abundance data from surface sediment samples collected in the Nordic Seas, and the trained ANNs were applied to high-resolution down-core records from the eastern Norwegian Sea (cores HM79-4 and MD95-2011).…”

Section: Introductionmentioning

confidence: 99%

Late Pleistocene–Holocene radiolarian paleotemperatures in the Norwegian Sea based on artificial neural networks

Cortese

Dolven

Bjørklund

et al. 2005

Palaeogeography, Palaeoclimatology, Palaeoecology

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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 (SST 100 ) compared to 10 m (SST 10 ) 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 Bølling-Allerød, when it reaches values only found later during the warmest phase of the Holocene. The climatic transitions in and out of the Younger Dryas are very rapid and involve a change in SST 100 of 6.2 and 6.8 8C, taking place over 440 and 140 years, respectively. SST 100 remains at a maximum during the early Holocene, and this Radiolarian Holocene Optimum Temperature Interval (RHOTI) predates the commonly recognized middle Holocene Climatic Optimum (HCO). During the 8.2 ka event, SST 100 decreases by ca. 3 8C, and this episode marks the establishment of a cooling trend, roughly spanning the middle Holocene (until ca. 4.2 ka). Successively, since then and through the late Holocene, SST 100 follows instead a statistically significant warming trend. The general patterns of the reconstructed SSTs agree quite well with previously obtained results based on application of Imbrie and Kipp Transfer Functions (IKTF) to the same two cores for SST 0 . A statistically significant cyclic component of our SST record (period of 278 years) has been recognized. This is close to the de Vries or Suess cycle, linked to solar variability, and documented in a variety of other high-resolution Holocene records. D

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A high‐resolution radiolarian‐derived paleotemperature record for the Late Pleistocene‐Holocene in the Norwegian Sea

Cited by 54 publications

References 41 publications

Holocene trends in the foraminifer record from the Norwegian Sea and the North Atlantic Ocean

Holocene trends in the foraminifer record from the Norwegian Sea and the North Atlantic Ocean

The Holocene thermal maximum in the Nordic Seas: the impact of Greenland Ice Sheet melt and other forcings in a coupled atmosphere–sea-ice–ocean model

Late Pleistocene–Holocene radiolarian paleotemperatures in the Norwegian Sea based on artificial neural networks

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