Dust sources and deposition during the last glacial maximum and current climate: a comparison of model results with paleodata from ice cores and marine sediments

Mineral dust aerosols in the atmosphere have the potential to affect the global climate by influencing the radiative balance of the atmosphere and the supply of micronutrients to the ocean. Ice and marine sediment cores indicate that dust deposition from the atmosphere was at some locations 2–20 times greater during glacial periods, raising the possibility that mineral aerosols might have contributed to climate change on glacial-interglacial time scales. To address this question, we have used linked terrestrial biosphere, dust source, and atmospheric transport models to simulate the dust cycle in the atmosphere for current and last glacial maximum (LGM) climates. We obtain a 2.5-fold higher dust loading in the entire atmosphere and a twenty-fold higher loading in high latitudes, in LGM relative to present. Comparisons to a compilation of atmospheric dust deposition flux estimates for LGM and present in marine sediment and ice cores show that the simulated flux ratios are broadly in agreement with observations; differences suggest where further improvements in the simple dust model could be made. The simulated increase in high-latitude dustiness depends on the expansion of unvegetated areas, especially in the high latitudes and in central Asia, caused by a combination of increased aridity and low atmospheric [CO2]. The existence of these dust source areas at the LGM is supported by pollen data and loess distribution in the northern continents. These results point to a role for vegetation feedbacks, including climate effects and physiological effects of low [CO2], in modulating the atmospheric distribution of dust.

Probabilistic parameterisation of the surface mass balance--elevation feedback in regional climate model simulations of the Greenland ice sheet

We present a new parameterisation that relates surface mass balance (SMB: the sum of surface accumulation and surface ablation) to changes in surface elevation of the Greenland ice sheet (GrIS) for the MAR (Modèle Atmosphérique Régional: Fettweis, 2007) regional climate model. The motivation is to dynamically adjust SMB as the GrIS evolves, allowing us to force ice sheet models with SMB simulated by MAR while incorporating the SMB–elevation feedback, without the substantial technical challenges of coupling ice sheet and climate models. This also allows us to assess the effect of elevation feedback uncertainty on the GrIS contribution to sea level, using multiple global climate and ice sheet models, without the need for additional, expensive MAR simulations. We estimate this relationship separately below and above the equilibrium line altitude (ELA, separating negative and positive SMB) and for regions north and south of 77� N, from a set of MAR simulations in which we alter the ice sheet surface elevation. These give four “SMB lapse rates”, gradients that relate SMB changes to elevation changes. We assess uncertainties within a Bayesian framework, estimating probability distributions for each gradient from which we present best estimates and credibility intervals (CI) that bound 95% of the probability. Below the ELA our gradient estimates are mostly positive, because SMB usually increases with elevation: 0.56 (95% CI: −0.22 to 1.33) kgm−3 a−1 for the north, and 1.91 (1.03 to 2.61) kgm−3 a−1 for the south. Above the ELA, the gradients are much smaller in magnitude: 0.09 (−0.03 to 0.23) kgm−3 a−1 in the north, and 0.07 (−0.07 to 0.59) kgm−3 a−1 in the south, because SMB can either increase or decrease in response to increased elevation. Our statistically founded approach allows us to make probabilistic assessments for the effect of elevation feedback uncertainty on sea level projections (Edwards et al., 2014).

Effect of uncertainty in surface mass balance--elevation feedback on projections of the future sea level contribution of the Greenland ice sheet

We apply a new parameterisation of the Greenland ice sheet (GrIS) feedback between surface mass balance (SMB: the sum of surface accumulation and surface ablation) and surface elevation in the MAR regional climate model (Edwards et al., 2014) to projections of future climate change using five ice sheet models (ISMs). The MAR (Modèle Atmosphérique Régional: Fettweis, 2007) climate projections are for 2000–2199, forced by the ECHAM5 and HadCM3 global climate models (GCMs) under the SRES A1B emissions scenario. The additional sea level contribution due to the SMB– elevation feedback averaged over five ISM projections for ECHAM5 and three for HadCM3 is 4.3% (best estimate; 95% credibility interval 1.8–6.9 %) at 2100, and 9.6% (best estimate; 95% credibility interval 3.6–16.0 %) at 2200. In all results the elevation feedback is significantly positive, amplifying the GrIS sea level contribution relative to the MAR projections in which the ice sheet topography is fixed: the lower bounds of our 95% credibility intervals (CIs) for sea level contributions are larger than the “no feedback” case for all ISMs and GCMs. Our method is novel in sea level projections because we propagate three types of modelling uncertainty – GCM and ISM structural uncertainties, and elevation feedback parameterisation uncertainty – along the causal chain, from SRES scenario to sea level, within a coherent experimental design and statistical framework. The relative contributions to uncertainty depend on the timescale of interest. At 2100, the GCM uncertainty is largest, but by 2200 both the ISM and parameterisation uncertainties are larger. We also perform a perturbed parameter ensemble with one ISM to estimate the shape of the projected sea level probability distribution; our results indicate that the probability density is slightly skewed towards higher sea level contributions.

Development and application of mass-spectrometric methods for the quantification and characterization of organic compounds in ice cores

Eisbohrkerne stellen wertvolle Klimaarchive dar, da sie atmosphärisches Aerosol konservieren. Die Analyse chemischer Verbindungen als Bestandteil atmosphärischer Aerosole in Eisbohrkernen liefert wichtige Informationen über Umweltbedingungen und Klima der Vergangenheit. Zur Untersuchung der α-Dicarbonyle Glyoxal und Methylglyoxal in Eis- und Schneeproben wurde eine neue, sensitive Methode entwickelt, die die Stir Bar Sorptive Extraction (SBSE) mit der Hochleistungsflüssigchromatographie-Massenspektrometrie (HPLC-MS) kombiniert. Zur Analyse von Dicarbonsäuren in Eisbohrkernen wurde eine weitere Methode entwickelt, bei der die Festphasenextraktion mit starkem Anionenaustauscher zum Einsatz kommt. Die Methode erlaubt die Quantifizierung aliphatischer Dicarbonsäuren (≥ C6), einschließlich Pinsäure, sowie aromatischer Carbonsäuren (wie Phthalsäure und Vanillinsäure), wodurch die Bestimmung wichtiger Markerverbindungen für biogene und anthropogene Quellen ermöglicht wurde. Mit Hilfe der entwickelten Methoden wurde ein Eisbohrkern aus den Schweizer Alpen analysiert. Die ermittelten Konzentrationsverläufe der Analyten umfassen die Zeitspanne von 1942 bis 1993. Mittels einer Korrelations- und Hauptkomponentenanalyse konnte gezeigt werden, dass die organischen Verbindungen im Eis hauptsächlich durch Waldbrände und durch vom Menschen verursachte Schadstoffemissionen beeinflusst werden. Im Gegensatz dazu sind die Konzentrationsverläufe einiger Analyten auf den Mineralstaubtransport auf den Gletscher zurückzuführen. Zusätzlich wurde ein Screening der Eisbohrkernproben mittels ultrahochauflösender Massenspektrometrie durchgeführt. Zum ersten Mal wurden in diesem Rahmen auch Organosulfate und Nitrooxyorganosulfate in einem Eisbohrkern identifiziert.