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Abstract. Even though dissolved organic carbon (DOC) is the most active carbon (C) cycling in soil organic carbon (SOC) pools, it receives little attention from the global C budget. DOC fluxes are critical to aquatic ecosystem inputs and contribute to the C balance of terrestrial ecosystems, but few ecosystem models have attempted to integrate DOC dynamics into terrestrial C cycling. This study introduces a new process-based model, TRIPLEX-DOC, that is capable of estimating DOC dynamics in forest soils by incorporating both ecological drivers and biogeochemical processes. TRIPLEX-DOC was developed from Forest-DNDC, a biogeochemical model simulating C and nitrogen (N) dynamics, coupled with a new DOC process module that predicts metabolic transformations, sorption/desorption, and DOC leaching in forest soils. The model was validated against field observations of DOC concentrations and fluxes at white pine forest stands located in southern Ontario, Canada. The model was able to simulate seasonal dynamics of DOC concentrations and the magnitudes observed within different soil layers, as well as DOC leaching in the age sequence of these forests. Additionally, TRIPLEX-DOC estimated the effect of forest harvesting on DOC leaching, with a significant increase following harvesting, illustrating that land use change is of critical importance in regulating DOC leaching in temperate forests as an important source of C input to aquatic ecosystems.
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Abstract. The Last Glacial Maximum (LGM; 21 000 yr before present) was a period of low atmospheric greenhouse gas concentrations, when vast ice sheets covered large parts of North America and Europe. Paleoclimate reconstructions and modeling studies suggest that the atmospheric circulation was substantially altered compared to today, both in terms of its mean state and its variability. Here we present a suite of coupled model simulations designed to investigate both the separate and combined influences of the main LGM boundary condition changes (greenhouse gases, ice sheet topography and ice sheet albedo) on the mean state and variability of the atmospheric circulation as represented by sea level pressure (SLP) and 200-hPa zonal wind in the North Atlantic sector. We find that ice sheet topography accounts for most of the simulated changes during the LGM. Greenhouse gases and ice sheet albedo affect the SLP gradient in the North Atlantic, but the overall placement of high and low pressure centers is controlled by topography. Additional analysis shows that North Atlantic sea surface temperatures and sea ice edge position do not substantially influence the pattern of the climatological-mean SLP field, SLP variability or the position of the North Atlantic jet in the LGM.
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Abstract. Using four different climate models, we investigate sea level pressure variability in the extratropical North Atlantic in the preindustrial climate (1750 AD) and at the Last Glacial Maximum (LGM, 21 kyrs before present) in order to understand how changes in atmospheric circulation can affect signals recorded in climate proxies. In general, the models exhibit a significant reduction in interannual variance of sea level pressure at the LGM compared to pre-industrial simulations and this reduction is concentrated in winter. For the preindustrial climate, all models feature a similar leading mode of sea level pressure variability that resembles the leading mode of variability in the instrumental record: the North Atlantic Oscillation (NAO). In contrast, the leading mode of sea level pressure variability at the LGM is model dependent, but in each model different from that in the preindustrial climate. In each model, the leading (NAO-like) mode of variability explains a smaller fraction of the variance and also less absolute variance at the LGM than in the preindustrial climate. The models show that the relationship between atmospheric variability and surface climate (temperature and precipitation) variability change in different climates. Results are model-specific, but indicate that proxy signals at the LGM may be misinterpreted if changes in the spatial pattern and seasonality of surface climate variability are not taken into account.