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Researchers have proposed that a significant portion of the post-glacial rise in atmospheric CO could be due to the respiration of permafrost carbon stocks that formed over the course of glaciation. In this paper, we used the University of Victoria Earth System Climate Model v. 2.9 to simulate the deglacial and interglacial carbon cycle from the last glacial maximum to the present. The model's sensitivity to mid and high latitude terrestrial carbon storage is evaluated by including a 600 Pg C carbon pool parameterized to respire in concert with decreases in ice sheet surface area. The respiration of this stored carbon during the early stages of deglaciation had a large effect on the carbon cycle in these simulations, allowing atmospheric CO to increase by 40 ppmv in the model, with an additional 20 ppmv increase occurring in the case of a more realistic, prescribed CO radiative warming. These increases occurred prior to large-scale carbon uptake due to the reestablishment of boreal forests and peatlands in the proxy record (beginning in the early Holocene). Surprisingly, the large external carbon input to the atmosphere and oceans did not increase sediment dissolution and mean ocean alkalinity relative to a control simulation without the high latitude carbon reservoir. In addition, our simulations suggest that an early deglacial terrestrial carbon release may come closer to explaining some observed deglacial changes in deep-ocean carbonate concentrations than simulations without such a release. We conclude that the respiration of glacial soil carbon stores may have been an important contributor to the deglacial CO rise, particularly in the early stages of deglaciation. [ABSTRACT FROM AUTHOR]

Climate reconstructions from data sensitive to past climates provide estimates of what these climates were like. Comparing these reconstructions with simulations from climate models allows to validate the models used for future climate prediction. It has been shown that for fossil pollen data, gaining estimates by inverting a vegetation model allows inclusion of past changes in carbon dioxide values. As a new generation of dynamic vegetation model is available we have developed an inversion method for one model, LPJ-GUESS. When this novel method is used with high-resolution sediment it allows us to bypass the classic assumptions of (1) climate and pollen independence between samples and (2) equilibrium between the vegetation, represented as pollen, and climate. Our dynamic inversion method is based on a statistical model to describe the links among climate, simulated vegetation and pollen samples. The inversion is realised thanks to a particle filter algorithm. We perform a validation on 30 modern European sites and then apply the method to the sediment core of Meerfelder Maar (Germany), which covers the Holocene at a temporal resolution of approximately one sample per 30 years. We demonstrate that reconstructed temperatures are constrained. The reconstructed precipitation is less well constrained, due to the dimension considered (one precipitation by season), and the low sensitivity of LPJ-GUESS to precipitation changes. [ABSTRACT FROM AUTHOR]