Schneider, Birgit, Leduc, G. and Park, Wonsun (2010) Disentangling seasonal signals in Holocene climate trends by satellite-model-proxy integration. Paleoceanography, 25 . PA4217. DOI 10.1029/2009PA001893.

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Abstract

Past sea surface temperatures (SSTs) are routinely estimated from organic and inorganic remains of fossil phytoplankton or zooplankton organisms, recovered
from sea floor sediments. Potential seasonal biases of paleo proxies were intensely studied in the past, however, even for the two most commonly used paleo proxies for SST, UK0 37 and Mg/Ca ratios, a clear global picture does
not yet exist. In the present study we combine Holocene SST trends derived from UK0 37 and Mg/Ca ratios with results from idealized climate model simulations forced by changes in the orbital conguration, which represents the major climate driver over the last 10 kyrs. Such changes cause a spatio-temporal redistribution of incoming solar radiation resulting in a modulation of amplitude and phasing of the seasonal cycle. Considering that the climate signal
recorded by a plankton-based paleo proxy may be aected by the seasonal productivity cycle of the respective organism, we use the modern relationship between SST and marine net primary production (NPP), both obtained from satellite observations, to calculate a seasonality index (SI) as an
independent constraint to link modeled SST trends with proxy data. Although the climate model systematically underestimates Holocene SST trends, we find that seasonal productivity peaks of the phytoplankton-based UK0 37 result
in a preferential registering of the warm (cold) season in high (low) latitudes, as it was expected from the SI distribution. The overall smoother trends from the zooplankton-derived Mg/Ca-SSTs suggest a more integrated signal over longer time averages, which may also carry a seasonal bias, but the spatial pattern is less clear. Based on our ndings, careful multi-proxy approaches can actually go beyond the reconstruction of average climate trends, specifically allowing to resolve the evolution of seasonality.

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