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Uncertainty about the sensitivity of the climate system to changes in the Earth’s radiative balance constitutes a primary source of uncertainty for climate projections. Given the continuous increase in atmospheric greenhouse gas concentrations, constraining the uncertainty range in such type of sensitivity is of vital importance. A common measure for expressing this key characteristic for climate models is the climate sensitivity, defined as the simulated change in global-mean equilibrium temperature resulting from a doubling of atmospheric CO2 concentration. The broad range of climate sensitivity estimates (1.5-4.5°C as given in the last Assessment Report of the Intergovernmental Panel on Climate Change, 2001), inferred from comprehensive climate models, illustrates that the strength of simulated feedback mechanisms varies strongly among different models. The central goal of this thesis is to constrain uncertainty in climate sensitivity. For this objective we first generate a large ensemble of model simulations, covering different feedback strengths, and then request their consistency with present-day observational data and proxy-data from the Last Glacial Maximum (LGM). Our analyses are based on an ensemble of fully-coupled simulations, that were realized with a climate model of intermediate complexity (CLIMBER-2). These model versions cover a broad range of different climate sensitivities, ranging from 1.3 to 5.5°C, and have been generated by simultaneously perturbing a set of 11 model parameters. The analysis of the simulated model feedbacks reveals that the spread in climate sensitivity results from different realizations of the feedback strengths in water vapour, clouds, lapse rate and albedo. The calculated spread in the sum of all feedbacks spans almost the entire plausible range inferred from a sampling of more complex models. We show that the requirement for consistency between simulated pre-industrial climate and a set of seven global-mean data constraints represents a comparatively weak test for model sensitivity (the data constrain climate sensitivity to 1.3-4.9°C). Analyses of the simulated latitudinal profile and of the seasonal cycle suggest that additional present-day data constraints, based on these characteristics, do not further constrain uncertainty in climate sensitivity. The novel approach presented in this thesis consists in systematically combining a large set of LGM simulations with data information from reconstructed regional glacial cooling. Irrespective of uncertainties in model parameters and feedback strengths, the set of our model versions reveals a close link between the simulated warming due to a doubling of CO2, and the cooling obtained for the LGM. Based on this close relationship between past and future temperature evolution, we define a method (based on linear regression) that allows us to estimate robust 5-95% quantiles for climate sensitivity. We thus constrain the range of climate sensitivity to 1.3-3.5°C using proxy-data from the LGM at low and high latitudes. Uncertainties in glacial radiative forcing enlarge this estimate to 1.2-4.3°C, whereas the assumption of large structural uncertainties may increase the upper limit by an additional degree. Using proxy-based data constraints for tropical and Antarctic cooling we show that very different absolute temperature changes in high and low latitudes all yield very similar estimates of climate sensitivity. On the whole, this thesis highlights that LGM proxy-data information can offer an effective means of constraining the uncertainty range in climate sensitivity and thus underlines the potential of paleo-climatic data to reduce uncertainty in future climate projections.
Thawing of permafrost and the associated release of carbon constitutes a positive feedback in the climate system, elevating the effect of anthropogenic GHG emissions on global-mean temperatures. Multiple factors have hindered the quantification of this feedback, which was not included in climate carbon-cycle models which participated in recent model intercomparisons (such as the Coupled Carbon Cycle Climate Model Intercomparison Project - (CMIP)-M-4). There are considerable uncertainties in the rate and extent of permafrost thaw, the hydrological and vegetation response to permafrost thaw, the decomposition timescales of freshly thawed organic material, the proportion of soil carbon that might be emitted as carbon dioxide via aerobic decomposition or as methane via anaerobic decomposition, and in the magnitude of the high latitude amplification of global warming that will drive permafrost degradation. Additionally, there are extensive and poorly characterized regional heterogeneities in soil properties, carbon content, and hydrology. Here, we couple a new permafrost module to a reduced complexity carbon-cycle climate model, which allows us to perform a large ensemble of simulations. The ensemble is designed to span the uncertainties listed above and thereby the results provide an estimate of the potential strength of the feedback from newly thawed permafrost carbon. For the high CO2 concentration scenario (RCP8.5), 33-114 GtC (giga tons of Carbon) are released by 2100 (68% uncertainty range). This leads to an additional warming of 0.04-0.23 degrees C. Though projected 21st century permafrost carbon emissions are relatively modest, ongoing permafrost thaw and slow but steady soil carbon decomposition means that, by 2300, about half of the potentially vulnerable permafrost carbon stock in the upper 3 m of soil layer (600-1000 GtC) could be released as CO2, with an extra 1-4% being released as methane. Our results also suggest that mitigation action in line with the lower scenario RCP3-PD could contain Arctic temperature increase sufficiently that thawing of the permafrost area is limited to 9-23% and the permafrost-carbon induced temperature increase does not exceed 0.04-0.16 degrees C by 2300.